| from __future__ import annotations | |
| from math import floor as mfloor | |
| from sympy.polys.domains import ZZ, QQ | |
| from sympy.polys.matrices.exceptions import DMRankError, DMShapeError, DMValueError, DMDomainError | |
| def _ddm_lll(x, delta=QQ(3, 4), return_transform=False): | |
| if QQ(1, 4) >= delta or delta >= QQ(1, 1): | |
| raise DMValueError("delta must lie in range (0.25, 1)") | |
| if x.shape[0] > x.shape[1]: | |
| raise DMShapeError("input matrix must have shape (m, n) with m <= n") | |
| if x.domain != ZZ: | |
| raise DMDomainError("input matrix domain must be ZZ") | |
| m = x.shape[0] | |
| n = x.shape[1] | |
| k = 1 | |
| y = x.copy() | |
| y_star = x.zeros((m, n), QQ) | |
| mu = x.zeros((m, m), QQ) | |
| g_star = [QQ(0, 1) for _ in range(m)] | |
| half = QQ(1, 2) | |
| T = x.eye(m, ZZ) if return_transform else None | |
| linear_dependent_error = "input matrix contains linearly dependent rows" | |
| def closest_integer(x): | |
| return ZZ(mfloor(x + half)) | |
| def lovasz_condition(k: int) -> bool: | |
| return g_star[k] >= ((delta - mu[k][k - 1] ** 2) * g_star[k - 1]) | |
| def mu_small(k: int, j: int) -> bool: | |
| return abs(mu[k][j]) <= half | |
| def dot_rows(x, y, rows: tuple[int, int]): | |
| return sum([x[rows[0]][z] * y[rows[1]][z] for z in range(x.shape[1])]) | |
| def reduce_row(T, mu, y, rows: tuple[int, int]): | |
| r = closest_integer(mu[rows[0]][rows[1]]) | |
| y[rows[0]] = [y[rows[0]][z] - r * y[rows[1]][z] for z in range(n)] | |
| mu[rows[0]][:rows[1]] = [mu[rows[0]][z] - r * mu[rows[1]][z] for z in range(rows[1])] | |
| mu[rows[0]][rows[1]] -= r | |
| if return_transform: | |
| T[rows[0]] = [T[rows[0]][z] - r * T[rows[1]][z] for z in range(m)] | |
| for i in range(m): | |
| y_star[i] = [QQ.convert_from(z, ZZ) for z in y[i]] | |
| for j in range(i): | |
| row_dot = dot_rows(y, y_star, (i, j)) | |
| try: | |
| mu[i][j] = row_dot / g_star[j] | |
| except ZeroDivisionError: | |
| raise DMRankError(linear_dependent_error) | |
| y_star[i] = [y_star[i][z] - mu[i][j] * y_star[j][z] for z in range(n)] | |
| g_star[i] = dot_rows(y_star, y_star, (i, i)) | |
| while k < m: | |
| if not mu_small(k, k - 1): | |
| reduce_row(T, mu, y, (k, k - 1)) | |
| if lovasz_condition(k): | |
| for l in range(k - 2, -1, -1): | |
| if not mu_small(k, l): | |
| reduce_row(T, mu, y, (k, l)) | |
| k += 1 | |
| else: | |
| nu = mu[k][k - 1] | |
| alpha = g_star[k] + nu ** 2 * g_star[k - 1] | |
| try: | |
| beta = g_star[k - 1] / alpha | |
| except ZeroDivisionError: | |
| raise DMRankError(linear_dependent_error) | |
| mu[k][k - 1] = nu * beta | |
| g_star[k] = g_star[k] * beta | |
| g_star[k - 1] = alpha | |
| y[k], y[k - 1] = y[k - 1], y[k] | |
| mu[k][:k - 1], mu[k - 1][:k - 1] = mu[k - 1][:k - 1], mu[k][:k - 1] | |
| for i in range(k + 1, m): | |
| xi = mu[i][k] | |
| mu[i][k] = mu[i][k - 1] - nu * xi | |
| mu[i][k - 1] = mu[k][k - 1] * mu[i][k] + xi | |
| if return_transform: | |
| T[k], T[k - 1] = T[k - 1], T[k] | |
| k = max(k - 1, 1) | |
| assert all([lovasz_condition(i) for i in range(1, m)]) | |
| assert all([mu_small(i, j) for i in range(m) for j in range(i)]) | |
| return y, T | |
| def ddm_lll(x, delta=QQ(3, 4)): | |
| return _ddm_lll(x, delta=delta, return_transform=False)[0] | |
| def ddm_lll_transform(x, delta=QQ(3, 4)): | |
| return _ddm_lll(x, delta=delta, return_transform=True) | |