From 86df1c6c622aba1eefa5a0c437441e19acf88f63 Mon Sep 17 00:00:00 2001 From: Samuel Vangu Date: Sun, 16 Aug 2026 22:02:12 +0200 Subject: [PATCH] Add Kronecker sampler as well as tests and a demonstrating notebook --- examples/kronecker.ipynb | 482 +++++++++++++++++++++++++++++++ src/grid/__init__.py | 12 +- src/grid/kronecker.py | 222 ++++++++++++++ src/grid/tests/test_kronecker.py | 276 ++++++++++++++++++ 4 files changed, 986 insertions(+), 6 deletions(-) create mode 100644 examples/kronecker.ipynb create mode 100644 src/grid/kronecker.py create mode 100644 src/grid/tests/test_kronecker.py diff --git a/examples/kronecker.ipynb b/examples/kronecker.ipynb new file mode 100644 index 00000000..f0f5e9d2 --- /dev/null +++ b/examples/kronecker.ipynb @@ -0,0 +1,482 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "56b37b54", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/theochem/grid/blob/master/examples/Kronecker.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "3f6bce1a", + "metadata": {}, + "source": [ + "# Kronecker (Weyl) Sequences\n", + "\n", + "The [Kronecker](https://grid.qcdevs.org/pyapi/grid.kronecker.html#grid.kronecker.Kronecker)\n", + "grid can be used for integration over a (hyper)cubic or parallelepiped domain.\n", + "Kronecker sequences (Kronecker, 1884; Weyl, 1916) are among the simplest\n", + "low-discrepancy constructions: given an irrational number $\\alpha$,\n", + "the sequence $x_i = \\{i\\alpha\\}$ (fractional part) equidistributes on\n", + "$[0,1)$ as $i \\to \\infty$. This class uses $\\alpha_j = \\sqrt{p_j}$, the\n", + "square roots of the first $d$ primes, for the $d$ coordinates.\n", + "\n", + "Weyl's classical equidistribution theory gives these sequences a particular\n", + "strength: for **periodic** functions on the torus $[0,1)^d$, Weyl\n", + "sums built from $\\{i\\boldsymbol{\\alpha}\\}$ converge especially fast --\n", + "substantially faster than for a generic smooth, non-periodic integrand.\n", + "This notebook builds a Kronecker design, visualizes it in three dimensions,\n", + "and compares it against baselines already in `grid` on two integrands from\n", + "computational chemistry -- one periodic, one not -- to see honestly where\n", + "this advantage shows up." + ] + }, + { + "cell_type": "markdown", + "id": "c8bf30c0", + "metadata": {}, + "source": [ + "## Initialization of Kronecker\n", + "\n", + "`Kronecker` is initialized by specifying:\n", + "\n", + "1. `n_points` -- the number of integration points $N$. No power-of-2 or\n", + " other special constraint is required (unlike `Sobol` or `Lattice`).\n", + "2. `dimension` -- the dimension $d$ of the integration domain.\n", + "3. `seed` (optional) -- for reproducibility, used only when `randomize=True`.\n", + "4. `randomize` (optional, default `True`) -- if `True`, applies a single\n", + " random shift to the whole sequence, reduced modulo 1 (a\n", + " Cranley-Patterson rotation). If `False`, generates the deterministic\n", + " sequence $x_i = \\{i\\boldsymbol{\\alpha}\\}$, whose first point is always\n", + " the origin.\n", + "5. `origin` and `axes` (optional) -- to map the design from the unit\n", + " hypercube $[0,1)^d$ onto an arbitrary parallelepiped.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d7813235", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of points: 16\n", + "Dimension: 2\n", + "Points are in [0, 1)^2: True\n", + "\n", + "Points:\n", + "[[0.63696169 0.26978671]\n", + " [0.05117525 0.00183752]\n", + " [0.46538881 0.73388833]\n", + " [0.87960237 0.46593914]\n", + " [0.29381594 0.19798994]\n", + " [0.7080295 0.93004075]\n", + " [0.12224306 0.66209156]\n", + " [0.53645662 0.39414237]\n", + " [0.95067019 0.12619317]\n", + " [0.36488375 0.85824398]\n", + " [0.77909731 0.59029479]\n", + " [0.19331087 0.3223456 ]\n", + " [0.60752444 0.0543964 ]\n", + " [0.021738 0.78644721]\n", + " [0.43595156 0.51849802]\n", + " [0.85016512 0.25054883]]\n" + ] + } + ], + "source": [ + "from grid.kronecker import Kronecker\n", + "import numpy as np\n", + "\n", + "kro = Kronecker(n_points=16, dimension=2, seed=0)\n", + "print(f\"Number of points: {kro.size}\")\n", + "print(f\"Dimension: {kro.dimension}\")\n", + "print(f\"Points are in [0, 1)^2: {(kro.points >= 0).all() and (kro.points < 1).all()}\")\n", + "print()\n", + "print(\"Points:\")\n", + "print(kro.points)" + ] + }, + { + "cell_type": "markdown", + "id": "90e2a08b", + "metadata": {}, + "source": [ + "With `randomize=False`, the sequence is fully deterministic, and its first\n", + "point is always the origin -- since $x_0 = \\{0 \\cdot \\boldsymbol{\\alpha}\\} = 0$\n", + "regardless of $\\boldsymbol{\\alpha}$." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "02c8f13a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First point, randomize=False: [0. 0.]\n", + "First point, randomize=True: [0.63696169 0.26978671]\n" + ] + } + ], + "source": [ + "kro_plain = Kronecker(n_points=16, dimension=2, seed=0, randomize=False)\n", + "kro_shifted = Kronecker(n_points=16, dimension=2, seed=0, randomize=True)\n", + "\n", + "print(\"First point, randomize=False:\", kro_plain.points[0])\n", + "print(\"First point, randomize=True: \", kro_shifted.points[0])" + ] + }, + { + "cell_type": "markdown", + "id": "09e9eb64", + "metadata": {}, + "source": [ + "By default, points live on the unit hypercube $[0,1)^d$. Passing `origin`\n", + "and `axes` maps the design onto an arbitrary parallelepiped." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4e913141", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Points now lie in [1, 4) x [2, 2.5): True\n", + "Weight per point (volume / N): 0.0938\n" + ] + } + ], + "source": [ + "origin = np.array([1.0, 2.0])\n", + "axes = np.array([[3.0, 0.0], [0.0, 0.5]])\n", + "kro_mapped = Kronecker(n_points=16, dimension=2, seed=0, origin=origin, axes=axes)\n", + "\n", + "print(f\"Points now lie in [1, 4) x [2, 2.5): \"\n", + " f\"{(kro_mapped.points[:, 0] >= 1).all() and (kro_mapped.points[:, 0] < 4).all()}\")\n", + "print(f\"Weight per point (volume / N): {kro_mapped.weights[0]:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "504781a9", + "metadata": {}, + "source": [ + "## A 3D Scatter Plot of the Design\n", + "\n", + "Because $x_i = \\{i\\boldsymbol{\\alpha}\\}$ never depends on the total number of\n", + "points requested, a design of $N$ points is always an exact prefix of a\n", + "design of any larger size with the same seed -- points already computed\n", + "never move or get discarded as more are added." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7421c281", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa: F401 (registers 3D projection)\n", + "\n", + "kro3d = Kronecker(n_points=500, dimension=3, seed=0)\n", + "\n", + "fig = plt.figure(figsize=(7, 6))\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", + "ax.scatter(\n", + " kro3d.points[:, 0], kro3d.points[:, 1], kro3d.points[:, 2],\n", + " s=15, color=\"#2E5C8A\", alpha=0.7, depthshade=True,\n", + ")\n", + "ax.set_xlabel(\"x\")\n", + "ax.set_ylabel(\"y\")\n", + "ax.set_zlabel(\"z\")\n", + "ax.set_title(f\"Kronecker: {kro3d.size} points in $[0,1)^3$\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6a0e80c6", + "metadata": {}, + "source": [ + "## Two Functions From Computational Chemistry\n", + "\n", + "Kronecker's classical strength, via Weyl's equidistribution theory, is on\n", + "**periodic** integrands -- not smoothness or additivity specifically. To see\n", + "this honestly, we compare a periodic integrand against a smooth but\n", + "non-periodic one.\n", + "\n", + "**1. A periodic lattice potential (periodic).** Cosine potentials are a\n", + "standard simplified model for a periodic crystal lattice potential in\n", + "solid-state chemistry (e.g. tight-binding and plane-wave models of periodic\n", + "solids):\n", + "\n", + "$$f(\\mathbf{x}) = \\prod_{i=1}^d \\left(1 + \\cos(2\\pi k_i x_i)\\right), \\qquad k_i \\in \\mathbb{Z}$$\n", + "\n", + "This is exactly periodic on $[0,1)^d$: $f(\\mathbf{x} + \\mathbf{e}_j) = f(\\mathbf{x})$\n", + "for each coordinate direction.\n", + "\n", + "**2. A product of Gaussian-type functions (smooth, non-periodic).** The same\n", + "Gaussian-type-orbital-like model used in the companion notebooks:\n", + "\n", + "$$g(\\mathbf{x}) = \\prod_{i=1}^d \\exp(-\\alpha_i x_i^2)$$\n", + "\n", + "Smooth, but $g(0) \\neq g(1)$ in each coordinate -- not periodic." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5a1a20a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact integral of the periodic potential: 1.000000\n", + "Exact integral of the GTO product: 0.349220\n" + ] + } + ], + "source": [ + "from scipy.special import erf\n", + "\n", + "rng = np.random.default_rng(0)\n", + "DIM = 3\n", + "\n", + "k_freq = np.array([1, 2, 3]) # integer frequencies -> exactly periodic on [0,1)\n", + "alpha = rng.uniform(0.5, 3.0, size=DIM)\n", + "\n", + "\n", + "def periodic_potential(x):\n", + " # Simplified periodic lattice potential: prod_i (1 + cos(2*pi*k_i*x_i)).\n", + " return np.prod(1 + np.cos(2 * np.pi * k_freq[None, :] * x), axis=1)\n", + "\n", + "\n", + "def periodic_potential_exact():\n", + " # int_0^1 (1 + cos(2*pi*k*x)) dx = 1 for any nonzero integer k, so the\n", + " # d-dimensional integral is 1^d = 1.\n", + " return 1.0\n", + "\n", + "\n", + "def gto_product(x):\n", + " # Product of Gaussian-type functions: f(x) = prod_i exp(-alpha_i x_i^2).\n", + " return np.exp(-(x**2 * alpha[None, :])).prod(axis=1)\n", + "\n", + "\n", + "def gto_product_exact():\n", + " # Exact integral over [0,1]^d, via the error function.\n", + " per_dim = (np.sqrt(np.pi) / (2 * np.sqrt(alpha))) * erf(np.sqrt(alpha))\n", + " return per_dim.prod()\n", + "\n", + "\n", + "print(f\"Exact integral of the periodic potential: {periodic_potential_exact():.6f}\")\n", + "print(f\"Exact integral of the GTO product: {gto_product_exact():.6f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "08dfbb97", + "metadata": {}, + "source": [ + "## Comparing Against Baselines Already in `grid`\n", + "\n", + "We use `Tensor1DGrids`, built from three `Trapezoidal` 1D grids (`onedgrid`),\n", + "as a structured baseline already present in the library. Like `UniformGrid`,\n", + "`Tensor1DGrids` inherits from `_HyperRectangleGrid` and is restricted to two\n", + "or three dimensions -- one more reason to run this comparison at $d=3$.\n", + "`Trapezoidal` is defined on $[-1,1]$, so its points and weights are remapped\n", + "onto $[0,1]^3$ with a simple affine transformation.\n", + "\n", + "**A note before looking at the results:** the trapezoidal rule is famously\n", + "*spectrally accurate* for smooth periodic functions integrated over a full\n", + "period -- this is a well-known fact of classical numerical analysis, unrelated\n", + "to any quasi-Monte Carlo property. We should therefore expect\n", + "`Tensor1DGrids` to be essentially exact on the periodic integrand\n", + "regardless of which point-cloud method we compare it to. The fair,\n", + "informative comparison for Kronecker's own equidistribution property is\n", + "against **Monte Carlo**, not against a quadrature rule already specialized\n", + "for periodic functions." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "06ac86ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n" + ] + } + ], + "source": [ + "from grid.onedgrid import Trapezoidal\n", + "from grid.cubic import Tensor1DGrids\n", + "from grid.basegrid import Grid\n", + "\n", + "N_VALUES_PER_AXIS = [3, 5, 7, 9, 12]\n", + "N_TRIALS = 15\n", + "\n", + "exact_per = periodic_potential_exact()\n", + "exact_gto = gto_product_exact()\n", + "\n", + "kro_err_per, mc_err_per, tensor_err_per = [], [], []\n", + "kro_err_gto, mc_err_gto, tensor_err_gto = [], [], []\n", + "n_points_list = []\n", + "\n", + "for m in N_VALUES_PER_AXIS:\n", + " N = m ** DIM\n", + " n_points_list.append(N)\n", + "\n", + " trial_kro_per, trial_mc_per = [], []\n", + " trial_kro_gto, trial_mc_gto = [], []\n", + " for trial in range(N_TRIALS):\n", + " kro = Kronecker(n_points=N, dimension=DIM, seed=trial)\n", + " trial_kro_per.append(abs(kro.integrate(periodic_potential(kro.points)) - exact_per))\n", + " trial_kro_gto.append(abs(kro.integrate(gto_product(kro.points)) - exact_gto))\n", + "\n", + " mc_points = np.random.default_rng(1000 + trial).random((N, DIM))\n", + " mc_weights = np.full(N, 1.0 / N)\n", + " mc_grid = Grid(mc_points, mc_weights)\n", + " trial_mc_per.append(abs(mc_grid.integrate(periodic_potential(mc_points)) - exact_per))\n", + " trial_mc_gto.append(abs(mc_grid.integrate(gto_product(mc_points)) - exact_gto))\n", + "\n", + " kro_err_per.append(np.mean(trial_kro_per))\n", + " mc_err_per.append(np.mean(trial_mc_per))\n", + " kro_err_gto.append(np.mean(trial_kro_gto))\n", + " mc_err_gto.append(np.mean(trial_mc_gto))\n", + "\n", + " oned = Trapezoidal(m)\n", + " tensor = Tensor1DGrids(oned, oned, oned)\n", + " tensor_points = (tensor.points + 1) / 2\n", + " tensor_weights = tensor.weights / (2 ** DIM)\n", + " tensor_grid = Grid(tensor_points, tensor_weights)\n", + " tensor_err_per.append(abs(tensor_grid.integrate(periodic_potential(tensor_points)) - exact_per))\n", + " tensor_err_gto.append(abs(tensor_grid.integrate(gto_product(tensor_points)) - exact_gto))\n", + "\n", + "print(\"Done.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7223a30a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "axes[0].loglog(n_points_list, mc_err_per, \"o--\", color=\"#B0413E\", label=\"Monte Carlo\")\n", + "axes[0].loglog(n_points_list, tensor_err_per, \"^-\", color=\"#4C9A5B\", label=\"Tensor1DGrids (Trapezoidal)\")\n", + "axes[0].loglog(n_points_list, kro_err_per, \"s-\", color=\"#2E5C8A\", label=\"Kronecker\")\n", + "axes[0].set_xlabel(\"N (number of points)\")\n", + "axes[0].set_ylabel(\"Mean absolute error\")\n", + "axes[0].set_title(r\"Periodic: $\\prod_i (1+\\cos(2\\pi k_i x_i))$\" + f\" (d={DIM})\")\n", + "axes[0].legend()\n", + "axes[0].grid(True, which=\"both\", alpha=0.3)\n", + "\n", + "axes[1].loglog(n_points_list, mc_err_gto, \"o--\", color=\"#B0413E\", label=\"Monte Carlo\")\n", + "axes[1].loglog(n_points_list, tensor_err_gto, \"^-\", color=\"#4C9A5B\", label=\"Tensor1DGrids (Trapezoidal)\")\n", + "axes[1].loglog(n_points_list, kro_err_gto, \"s-\", color=\"#2E5C8A\", label=\"Kronecker\")\n", + "axes[1].set_xlabel(\"N (number of points)\")\n", + "axes[1].set_ylabel(\"Mean absolute error\")\n", + "axes[1].set_title(r\"Smooth, non-periodic: $\\prod_i \\exp(-\\alpha_i x_i^2)$\" + f\" (d={DIM})\")\n", + "axes[1].legend()\n", + "axes[1].grid(True, which=\"both\", alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "04f87adf", + "metadata": {}, + "source": [ + "As anticipated, `Tensor1DGrids` is essentially exact on the periodic\n", + "potential (errors at the level of machine precision) -- a property of the\n", + "trapezoidal rule on periodic functions, not something any point-cloud method\n", + "can be expected to match. The informative comparison here is `Kronecker`\n", + "against `Monte Carlo`: on the periodic integrand, Kronecker's error is\n", + "consistently 5-15 times smaller than Monte Carlo's, directly reflecting the\n", + "equidistribution advantage Weyl's theory predicts for periodic functions.\n", + "\n", + "On the smooth, non-periodic GTO product, Kronecker in fact outperforms\n", + "*both* baselines at every sample size tested here -- including\n", + "`Tensor1DGrids` -- though this should not be over-generalized: the\n", + "trapezoidal rule's disadvantage on this particular integrand comes from the\n", + "domain boundary not matching a period, and the comparison could look\n", + "different for another smooth function or a finer structured grid. Taken\n", + "together, the two panels give an honest picture: Kronecker is a simple,\n", + "low-cost point-cloud method that is consistently competitive with Monte\n", + "Carlo and, on these examples, with a structured baseline too -- while\n", + "remaining, unlike `Tensor1DGrids`, usable in dimensions where a structured\n", + "tensor grid would be computationally infeasible. Its equidistribution\n", + "advantage is most pronounced, and best theoretically motivated, for\n", + "periodic integrands specifically." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/grid/__init__.py b/src/grid/__init__.py index 952e79a9..7fa0c3ed 100644 --- a/src/grid/__init__.py +++ b/src/grid/__init__.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- # GRID is a numerical integration module for quantum chemistry. # # Copyright (C) 2011-2019 The GRID Development Team @@ -19,21 +18,22 @@ # along with this program; if not, see # -- """Grid Module.""" -# ruff: noqa: F401,F403 + +# ruff: noqa: F403 from grid.angular import * from grid.atomgrid import * from grid.basegrid import * from grid.becke import * +from grid.coulomb import * from grid.cubic import * from grid.hirshfeld import * -from grid.angular import * +from grid.kronecker import * from grid.molgrid import * +from grid.ngrid import * from grid.ode import * from grid.onedgrid import * from grid.periodicgrid import * -from grid.rtransform import * -from grid.ngrid import * -from grid.coulomb import * from grid.robust_poisson import * +from grid.rtransform import * diff --git a/src/grid/kronecker.py b/src/grid/kronecker.py new file mode 100644 index 00000000..e22dc873 --- /dev/null +++ b/src/grid/kronecker.py @@ -0,0 +1,222 @@ +# GRID is a numerical integration module for quantum chemistry. +# +# Copyright (C) 2011-2019 The GRID Development Team +# +# This file is part of GRID. +# +# GRID is free software; you can redistribute it and/or +# modify it under the terms of the GNU General Public License +# as published by the Free Software Foundation; either version 3 +# of the License, or (at your option) any later version. +# +# GRID is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program; if not, see +# -- +r"""Kronecker (Weyl) Sequences for Integration on (Hyper)Cubic Grids.""" + +import numpy as np +from sympy import sieve + +from grid.basegrid import Grid + + +class Kronecker(Grid): + r"""Kronecker (Weyl) sequence for integration on a (hyper)cubic grid. + + Kronecker sequences are one of the simplest low-discrepancy constructions: + given a vector of irrational numbers :math:`\boldsymbol{\alpha} + \in \mathbb{R}^d`, the sequence is + + .. math:: + \mathbf{x}_i = \{ i\, \boldsymbol{\alpha} \} \quad \text{for } i = 0, \ldots, N-1 + + where :math:`\{y\}` denotes the fractional part of :math:`y`, applied + componentwise. This class uses :math:`\alpha_j = \sqrt{p_j}`, the square + roots of the first :math:`d` prime numbers, a standard choice: square + roots of distinct primes are irrational and free of simple rational + relations to one another, which keeps the components of the sequence + from lining up in low-dimensional resonances. + + With ``randomize=True``, a single random shift is added to every point + and reduced modulo 1 (Cranley & Patterson, 1976) -- a common + randomization technique for low-discrepancy sequences that preserves + their equidistribution properties while enabling randomized error + estimates. + + The integration weights are all equal to :math:`V/N`, where :math:`V` is the volume + of the integration domain. + + References + ---------- + - Kronecker, L. (1884). Naeherungsweise ganzzahlige Aufloesung linearer + Gleichungen. Berliner Sitzungsberichte, 1179-1193, 1271-1299. + - Cranley, R., & Patterson, T. N. L. (1976). Randomization of number + theoretic methods for multiple integration. SIAM Journal on Numerical + Analysis, 13(6), 904-914. + + """ + + def __init__( + self, + n_points, + dimension, + seed=None, + randomize=True, + origin=None, + axes=None, + ): + r"""Construct a Kronecker grid. + + Parameters + ---------- + n_points : int + Number of integration points :math:`N`. + dimension : int + Dimension :math:`d` of the integration domain. + seed : int, optional + Seed for the random number generator, used only when + ``randomize=True``. + randomize : bool, optional + If True (default), applies a random Cranley-Patterson shift to + the sequence. If False, generates the deterministic sequence + :math:`\mathbf{x}_i = \{i\boldsymbol{\alpha}\}`, whose first + point (:math:`i=0`) is always the origin. + origin : np.ndarray, shape (d,), optional + Origin of the hypercube. Defaults to zero vector. + axes : np.ndarray, shape (d, d), optional + Axes defining the hypercube (as row vectors). Defaults to identity + matrix (unit hypercube). The Kronecker points are first generated + on :math:`[0,1)^d` and then affine-transformed to the specified + parallelepiped. + + Raises + ------ + ValueError + If n_points or dimension is not a positive integer, or if + origin/axes have an incorrect shape, or if axes are not linearly + independent. + + """ + if n_points < 1: + raise ValueError(f"n_points must be >= 1, got {n_points}") + if dimension < 1: + raise ValueError(f"dimension must be >= 1, got {dimension}") + + # Generate Kronecker points in the unit cube [0, 1)^d + points_unit = self._generate_kronecker_points(n_points, dimension, seed, randomize) + + if origin is None: + origin = np.zeros(dimension) + else: + origin = np.asarray(origin, dtype=float) + if origin.shape != (dimension,): + raise ValueError(f"origin must have shape ({dimension},), got {origin.shape}") + + if axes is None: + axes = np.eye(dimension) + else: + axes = np.asarray(axes, dtype=float) + if axes.shape != (dimension, dimension): + raise ValueError( + f"axes must have shape ({dimension}, {dimension}), got {axes.shape}" + ) + if np.linalg.matrix_rank(axes) < dimension: + raise ValueError("axes must be linearly independent") + + # Map the unit cube points to the parallelepiped defined by origin and axes + # (affine transformation: x = origin + points_unit @ axes) + points = origin + points_unit @ axes + + # Volume of the parallelepiped + volume = np.abs(np.linalg.det(axes)) + + # Uniform weights + weights = np.full(n_points, volume / n_points) + + self._n_points = n_points + self._dimension = dimension + self._seed = seed + self._randomize = randomize + self._origin = origin + self._axes = axes + + super().__init__(points, weights) + + def _generate_kronecker_points(self, n_points, dimension, seed, randomize): + r"""Generate Kronecker points on the unit hypercube [0,1)^d. + + Parameters + ---------- + n_points : int + Number of points :math:`N`. + dimension : int + Dimension :math:`d`. + seed : int or None + Seed for the random number generator. + randomize : bool + If True, apply a random Cranley-Patterson shift. If False, + generate the deterministic sequence. + + Returns + ------- + np.ndarray, shape (N, d) + Kronecker points in the unit hypercube. + + """ + # sqrt of the first `dimension` primes, via sympy's public sieve API + # (sieve is 1-indexed: sieve[1] is the first prime, 2). + sieve.extend_to_no(dimension) + primes = np.array([sieve[i] for i in range(1, dimension + 1)]) + primes_sqrt = np.sqrt(primes) + + # x_i[j] = {i * sqrt(p_j)}, via broadcasting rather than materializing + # an explicit (n_points, dimension) tiled array of indices first. + result = np.mod(np.arange(n_points)[:, None] * primes_sqrt[None, :], 1) + + if randomize: + rng = np.random.default_rng(seed) + shift = rng.uniform(0, 1, size=dimension) + result = np.mod(result + shift, 1) + + return result + + def __getitem__(self, index): + """Return a plain Grid for a point subset""" + if isinstance(index, int): + return Grid(np.array([self.points[index]]), np.array([self.weights[index]])) + return Grid(np.array(self.points[index]), np.array(self.weights[index])) + + @property + def n_points(self): + """int: Number of points in the Kronecker design.""" + return self._n_points + + @property + def dimension(self): + """int: Dimension of the Kronecker grid.""" + return self._dimension + + @property + def seed(self): + """int or None: Seed used for reproducibility.""" + return self._seed + + @property + def randomize(self): + """bool: Whether a random Cranley-Patterson shift is applied (True) or not (False).""" + return self._randomize + + @property + def origin(self): + """np.ndarray: Origin of the integration domain.""" + return self._origin + + @property + def axes(self): + """np.ndarray: Axes defining the integration domain.""" + return self._axes diff --git a/src/grid/tests/test_kronecker.py b/src/grid/tests/test_kronecker.py new file mode 100644 index 00000000..9d604527 --- /dev/null +++ b/src/grid/tests/test_kronecker.py @@ -0,0 +1,276 @@ +# GRID is a numerical integration module for quantum chemistry. +# +# Copyright (C) 2011-2019 The GRID Development Team +# +# This file is part of GRID. +# +# GRID is free software; you can redistribute it and/or +# modify it under the terms of the GNU General Public License +# as published by the Free Software Foundation; either version 3 +# of the License, or (at your option) any later version. +# +# GRID is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program; if not, see +# -- +r"""Tests for Kronecker (Weyl) Sequences.""" + +import os +import tempfile +from unittest import TestCase + +import numpy as np +from numpy.testing import assert_allclose, assert_equal + +from grid.basegrid import Grid +from grid.kronecker import Kronecker + + +class TestKronecker(TestCase): + r"""Test Kronecker class.""" + + # ------------------------------------------------------------------ + # Validation errors + # ------------------------------------------------------------------ + + def test_raises_error_when_n_points_invalid(self): + r"""Test that n_points must be >= 1.""" + with self.assertRaises(ValueError) as err: + Kronecker(n_points=0, dimension=2) + self.assertIn("must be >= 1", str(err.exception)) + + def test_raises_error_when_dimension_invalid(self): + r"""Test that dimension must be >= 1.""" + with self.assertRaises(ValueError) as err: + Kronecker(n_points=100, dimension=0) + self.assertIn("must be >= 1", str(err.exception)) + + def test_raises_error_for_invalid_origin(self): + r"""Test that origin must have correct shape.""" + with self.assertRaises(ValueError) as err: + Kronecker(n_points=100, dimension=3, origin=np.array([0, 0])) + self.assertIn("origin must have shape (3,)", str(err.exception)) + + def test_raises_error_for_invalid_axes(self): + r"""Test that axes must have correct shape.""" + with self.assertRaises(ValueError) as err: + Kronecker(n_points=100, dimension=3, axes=np.eye(2)) + self.assertIn("axes must have shape (3, 3)", str(err.exception)) + + def test_raises_error_for_singular_axes(self): + r"""Test that axes must be linearly independent.""" + singular_axes = np.array([[1, 0, 0], [2, 0, 0], [0, 0, 1]]) + with self.assertRaises(ValueError) as err: + Kronecker(n_points=100, dimension=3, axes=singular_axes) + self.assertIn("must be linearly independent", str(err.exception)) + + # ------------------------------------------------------------------ + # Basic properties, weights, domain mapping + # ------------------------------------------------------------------ + + def test_properties(self): + r"""Test that Kronecker properties are correctly set.""" + n_points, dimension = 100, 3 + kro = Kronecker(n_points=n_points, dimension=dimension, seed=0) + + assert_equal(kro.size, n_points) + assert_equal(kro.n_points, n_points) + assert_equal(kro.dimension, dimension) + assert_equal(kro.randomize, True) + assert_equal(kro.points.shape, (n_points, dimension)) + assert_equal(kro.weights.shape, (n_points,)) + assert_allclose(kro.origin, np.zeros(dimension)) + assert_allclose(kro.axes, np.eye(dimension)) + + def test_weights_are_equal(self): + r"""Test that all weights are equal to V/N.""" + n_points, dimension = 100, 2 + kro = Kronecker(n_points=n_points, dimension=dimension, seed=0) + + expected_weight = 1.0 / n_points + assert_allclose(kro.weights, np.full(n_points, expected_weight)) + + def test_weights_with_custom_axes(self): + r"""Test that weights scale with volume.""" + n_points, dimension = 100, 2 + axes = np.array([[2.0, 0.0], [0.0, 2.0]]) + kro = Kronecker(n_points=n_points, dimension=dimension, axes=axes, seed=0) + + expected_weight = 4.0 / n_points + assert_allclose(kro.weights, np.full(n_points, expected_weight)) + + def test_points_in_unit_cube(self): + r"""Test that points are in [0, 1)^d for default parameters.""" + kro = Kronecker(n_points=100, dimension=3, seed=0) + assert np.all(kro.points >= 0.0) + assert np.all(kro.points < 1.0) + + def test_points_with_custom_origin_and_axes(self): + r"""Test that points are correctly transformed.""" + n_points, dimension = 100, 2 + origin = np.array([1.0, 2.0]) + axes = np.array([[0.5, 0.0], [0.0, 0.5]]) + kro = Kronecker(n_points=n_points, dimension=dimension, origin=origin, axes=axes, seed=0) + + assert np.all(kro.points[:, 0] >= 1.0) + assert np.all(kro.points[:, 0] < 1.5) + assert np.all(kro.points[:, 1] >= 2.0) + assert np.all(kro.points[:, 1] < 2.5) + + def test_integration_of_constant_function(self): + r"""Test integration of f(x) = 1 gives volume.""" + n_points, dimension = 2048, 3 + axes = np.diag([2.0, 3.0, 4.0]) # Volume = 24 + kro = Kronecker(n_points=n_points, dimension=dimension, axes=axes, seed=0) + + func_vals = np.ones(n_points) + integral = kro.integrate(func_vals) + assert_allclose(integral, 24.0, rtol=1e-10) + + def test_integration_of_linear_function(self): + r"""Test integration of f(x) = x_1 + x_2 on unit square.""" + n_points, dimension = 4096, 2 + kro = Kronecker(n_points=n_points, dimension=dimension, seed=0) + + func_vals = kro.points[:, 0] + kro.points[:, 1] + integral = kro.integrate(func_vals) + + # Exact integral over [0,1]^2: int_0^1 int_0^1 (x+y) dx dy = 1 + assert_allclose(integral, 1.0, rtol=1e-2) + + def test_save_and_load(self): + r"""Test saving Kronecker grid to file.""" + kro = Kronecker(n_points=100, dimension=2, seed=0) + + fd, filename = tempfile.mkstemp(suffix=".npz") + os.close(fd) + + try: + kro.save(filename) + loaded = np.load(filename) + assert_allclose(loaded["points"], kro.points) + assert_allclose(loaded["weights"], kro.weights) + loaded.close() + finally: + if os.path.exists(filename): + os.unlink(filename) + + def test_different_dimensions(self): + r"""Test Kronecker in different dimensions.""" + for dimension in [1, 2, 3, 5, 10]: + n_points = 100 + kro = Kronecker(n_points=n_points, dimension=dimension, seed=0) + assert_equal(kro.dimension, dimension) + assert_equal(kro.points.shape, (n_points, dimension)) + + def test_n_points_need_not_be_special(self): + r"""Test that Kronecker accepts any n_points (no power-of-2 or other constraint).""" + for n_points in [7, 100, 123, 1000]: + kro = Kronecker(n_points=n_points, dimension=3, seed=0) + assert_equal(kro.points.shape, (n_points, 3)) + + def test_reproducibility_same_seed(self): + r"""Test that the same seed gives identical points.""" + kro1 = Kronecker(n_points=30, dimension=2, seed=42) + kro2 = Kronecker(n_points=30, dimension=2, seed=42) + assert_allclose(kro1.points, kro2.points) + + def test_different_seeds_give_different_points(self): + r"""Test that different seeds give different points when randomize=True.""" + kro1 = Kronecker(n_points=30, dimension=2, seed=42) + kro2 = Kronecker(n_points=30, dimension=2, seed=43) + assert not np.allclose(kro1.points, kro2.points) + + def test_getitem_returns_plain_grid(self): + r"""Test that indexing returns a plain Grid, not a Kronecker instance.""" + kro = Kronecker(n_points=50, dimension=2, seed=0) + + single = kro[3] + assert isinstance(single, Grid) + assert not isinstance(single, Kronecker) + assert_equal(single.points.shape, (1, 2)) + + subset = kro[5:10] + assert isinstance(subset, Grid) + assert not isinstance(subset, Kronecker) + assert_equal(subset.points.shape, (5, 2)) + + # ------------------------------------------------------------------ + # Properties SPECIFIC to Kronecker sequences + # ------------------------------------------------------------------ + + def test_matches_sqrt_prime_construction(self): + r"""Test the construction against an independent calculation using sqrt(2),sqrt(3), sqrt(5). + + This checks the defining formula directly: x_i[j] = {i * sqrt(p_j)}, + where p_j is the j-th prime, rather than only checking downstream + properties of the generated points. + """ + n_points, dimension = 20, 3 + kro = Kronecker(n_points=n_points, dimension=dimension, randomize=False) + + p1, p2, p3 = np.sqrt(2), np.sqrt(3), np.sqrt(5) + expected = np.array([[(i * p1) % 1, (i * p2) % 1, (i * p3) % 1] for i in range(n_points)]) + assert_allclose(kro.points, expected) + + def test_first_point_is_origin_when_unrandomized(self): + r"""Test that the first point (index 0) is the origin when randomize=False. + + This follows directly from the defining formula: x_0 = {0 * alpha} = 0 + for any choice of alpha, so the first point is always the origin + before a random shift is applied. + """ + n_points, dimension = 100, 3 + origin = np.array([1.0, 2.0, 3.0]) + kro = Kronecker( + n_points=n_points, + dimension=dimension, + origin=origin, + randomize=False, + seed=0, + ) + assert_allclose(kro.points[0], origin) + + def test_randomize_true_and_false_differ(self): + r"""Test that randomize=True and randomize=False give different points. + + With randomize=True, a random Cranley-Patterson shift is applied, so + even the first point of the sequence is no longer the origin. + """ + n_points, dimension = 100, 3 + kro_shifted = Kronecker(n_points=n_points, dimension=dimension, seed=1, randomize=True) + kro_plain = Kronecker(n_points=n_points, dimension=dimension, seed=1, randomize=False) + + assert not np.allclose(kro_shifted.points, kro_plain.points) + assert not np.allclose(kro_shifted.points[0], np.zeros(dimension)) + + def test_prefix_is_identical_regardless_of_total_n_points(self): + r"""Test that the first N points do not depend on the total number requested. + + Because x_i = {i * alpha} depends only on the index i and not on the + total sample size N, a design of size N is an exact prefix of a + design of size M > N with the same seed and randomization -- unlike + constructions whose point positions are defined relative to N itself. + """ + n_points, dimension = 100, 2 + kro_n = Kronecker(n_points=n_points, dimension=dimension, seed=0, randomize=False) + kro_2n = Kronecker(n_points=2 * n_points, dimension=dimension, seed=0, randomize=False) + + assert_allclose(kro_n.points, kro_2n.points[:n_points]) + + def test_prefix_property_holds_with_randomization(self): + r"""Test that the prefix property also holds under a Cranley-Patterson shift. + + The same random shift is applied to every point regardless of the + total sample size, so the nesting property survives randomization + as long as the seed is the same. + """ + n_points, dimension = 100, 2 + kro_n = Kronecker(n_points=n_points, dimension=dimension, seed=7, randomize=True) + kro_2n = Kronecker(n_points=2 * n_points, dimension=dimension, seed=7, randomize=True) + + assert_allclose(kro_n.points, kro_2n.points[:n_points])