5[gBdZddlZddlmZmZddlmZdgZddZ ddZ y) z% Sketching-based Matrix Computations N)check_random_state rng_integers) csc_matrixclarkson_woodruff_transformct|}t|d||}tj|dz}|j ddg|}t |||f||f}|S)a{ Generate a matrix S which represents a Clarkson-Woodruff transform. Given the desired size of matrix, the method returns a matrix S of size (n_rows, n_columns) where each column has all the entries set to 0 except for one position which has been randomly set to +1 or -1 with equal probability. Parameters ---------- n_rows : int Number of rows of S n_columns : int Number of columns of S seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional If `seed` is None (or `np.random`), the `numpy.random.RandomState` singleton is used. If `seed` is an int, a new ``RandomState`` instance is used, seeded with `seed`. If `seed` is already a ``Generator`` or ``RandomState`` instance then that instance is used. Returns ------- S : (n_rows, n_columns) csc_matrix The returned matrix has ``n_columns`` nonzero entries. Notes ----- Given a matrix A, with probability at least 9/10, .. math:: \|SA\| = (1 \pm \epsilon)\|A\| Where the error epsilon is related to the size of S. r)shape)rrnparangechoicer)n_rows n_columnsseedrngrowscolssignsSs Q/var/www/html/bid-api/venv/lib/python3.12/site-packages/scipy/linalg/_sketches.py cwt_matrixrsbD T "C Q 2D 99Yq[ !D JJ2w *EE4&fi-@AA HcXt||jd|}|j|S)a Applies a Clarkson-Woodruff Transform/sketch to the input matrix. Given an input_matrix ``A`` of size ``(n, d)``, compute a matrix ``A'`` of size (sketch_size, d) so that .. math:: \|Ax\| \approx \|A'x\| with high probability via the Clarkson-Woodruff Transform, otherwise known as the CountSketch matrix. Parameters ---------- input_matrix : array_like Input matrix, of shape ``(n, d)``. sketch_size : int Number of rows for the sketch. seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional If `seed` is None (or `np.random`), the `numpy.random.RandomState` singleton is used. If `seed` is an int, a new ``RandomState`` instance is used, seeded with `seed`. If `seed` is already a ``Generator`` or ``RandomState`` instance then that instance is used. Returns ------- A' : array_like Sketch of the input matrix ``A``, of size ``(sketch_size, d)``. Notes ----- To make the statement .. math:: \|Ax\| \approx \|A'x\| precise, observe the following result which is adapted from the proof of Theorem 14 of [2]_ via Markov's Inequality. If we have a sketch size ``sketch_size=k`` which is at least .. math:: k \geq \frac{2}{\epsilon^2\delta} Then for any fixed vector ``x``, .. math:: \|Ax\| = (1\pm\epsilon)\|A'x\| with probability at least one minus delta. This implementation takes advantage of sparsity: computing a sketch takes time proportional to ``A.nnz``. Data ``A`` which is in ``scipy.sparse.csc_matrix`` format gives the quickest computation time for sparse input. >>> import numpy as np >>> from scipy import linalg >>> from scipy import sparse >>> rng = np.random.default_rng() >>> n_rows, n_columns, density, sketch_n_rows = 15000, 100, 0.01, 200 >>> A = sparse.rand(n_rows, n_columns, density=density, format='csc') >>> B = sparse.rand(n_rows, n_columns, density=density, format='csr') >>> C = sparse.rand(n_rows, n_columns, density=density, format='coo') >>> D = rng.standard_normal((n_rows, n_columns)) >>> SA = linalg.clarkson_woodruff_transform(A, sketch_n_rows) # fastest >>> SB = linalg.clarkson_woodruff_transform(B, sketch_n_rows) # fast >>> SC = linalg.clarkson_woodruff_transform(C, sketch_n_rows) # slower >>> SD = linalg.clarkson_woodruff_transform(D, sketch_n_rows) # slowest That said, this method does perform well on dense inputs, just slower on a relative scale. References ---------- .. [1] Kenneth L. Clarkson and David P. Woodruff. Low rank approximation and regression in input sparsity time. In STOC, 2013. .. [2] David P. Woodruff. Sketching as a tool for numerical linear algebra. In Foundations and Trends in Theoretical Computer Science, 2014. Examples -------- Create a big dense matrix ``A`` for the example: >>> import numpy as np >>> from scipy import linalg >>> n_rows, n_columns = 15000, 100 >>> rng = np.random.default_rng() >>> A = rng.standard_normal((n_rows, n_columns)) Apply the transform to create a new matrix with 200 rows: >>> sketch_n_rows = 200 >>> sketch = linalg.clarkson_woodruff_transform(A, sketch_n_rows, seed=rng) >>> sketch.shape (200, 100) Now with high probability, the true norm is close to the sketched norm in absolute value. >>> linalg.norm(A) 1224.2812927123198 >>> linalg.norm(sketch) 1226.518328407333 Similarly, applying our sketch preserves the solution to a linear regression of :math:`\min \|Ax - b\|`. >>> b = rng.standard_normal(n_rows) >>> x = linalg.lstsq(A, b)[0] >>> Ab = np.hstack((A, b.reshape(-1, 1))) >>> SAb = linalg.clarkson_woodruff_transform(Ab, sketch_n_rows, seed=rng) >>> SA, Sb = SAb[:, :-1], SAb[:, -1] >>> x_sketched = linalg.lstsq(SA, Sb)[0] As with the matrix norm example, ``linalg.norm(A @ x - b)`` is close to ``linalg.norm(A @ x_sketched - b)`` with high probability. >>> linalg.norm(A @ x - b) 122.83242365433877 >>> linalg.norm(A @ x_sketched - b) 166.58473879945151 r)rr dot) input_matrix sketch_sizerrs rrr8s-t ; 2 21 5tr#s'+ =# ( )' T{r