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Last active Jan 8, 2020
View smooth_periodic.ipynb Sorry, we cannot display this file.
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Created Dec 11, 2019
View benchmark.txt
 ---------------------------------------------------------------------------------- benchmark 'test_benchmark_assign_3x3': 6 tests --------------------------------------------------------------------------------- Name (time in us) Min Max Mean StdDev Median IQR Outliers OPS (Kops/s) Rounds Iterations ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- test_benchmark_assign_3x3[lapsolver] 21.8828 (1.0) 382.6180 (1.0) 27.1516 (1.0) 13.0945 (1.0) 23.1010 (1.0) 1.3541 (1.0) 1014;2011 36.8302 (1.0) 11318 1 test_benchmark_assign_3x3[lap] 41.3780 (1.89) 887.3888 (2.32) 52.0903 (1.92) 23.1687 (1.77) 45.7240 (1.98) 1.9629 (1.45) 853;1701 19
Created May 3, 2017
 #!/bin/bash baseurl="http://cvlab.hanyang.ac.kr/tracker_benchmark" wget "\$baseurl/datasets.html" cat datasets.html | grep '\.zip' | sed -e 's/\.zip".*/.zip/' | sed -e s'/.*"//' >files.txt cat files.txt | xargs -n 1 -P 8 -I {} wget -c "\$baseurl/{}"
Created Feb 8, 2017
Generate smooth functions in numpy
View smooth.py
 import numpy as np def covar1(m, s): s = float(s) r = np.arange(m, dtype=np.float) d = np.reshape(r, (m, 1)) - np.reshape(r, (1, m)) k = np.exp(-(0.5/s**2) * (d * d)) return k def smooth1(m, s):
Last active Jan 13, 2016
View benchmark_mul_circ_covar.m
 function benchmark() test(); m1 = 32; m2 = 48; k = 24; x = randn(m1, m2, k); af = covar1(randn(m1, m2, k)); a = ifft2(af, 'symmetric'); t = time_func(@() mul1(af, x));
Last active Jan 13, 2016
View mul_circ_covar.m
 % Computes circulant covariance of a and then multiplies x by covariance. % Both images are of size (m1, m2) with k channels. % % size(a) is [m1, m2, k] % size(x) is [m1, m2, k] function b = mul_circ_covar(a, x) [m1, m2, k] = size(x); % compute circulant covariance from shifts of a % sf(u1,u2,p,q) = conj(af(u1,u2,q)) * af(u1,u2,p)
Created Sep 1, 2015
View solve_rect.m
 function [x, mul_dx_dA, mul_dx_db] = solve_rect(A, b) % solve_rect returns x that minimises |A x - b|^2 % x = (A' A)^-1 A' b % and operators that compute derivatives with respect to A and b. % It uses a QR decomposition of A. % % Parameters: % A has size [m, n] with m >= n and rank(A) = n. % b has size [m, 1]. %
Last active Sep 1, 2015
View solve_square.m
 function [x, mul_dx_dA, mul_dx_db] = solve_square(A, b) % solve_square returns x = A^-1 b and operators that compute products with % derivatives with respect to A and b. It uses an LU decomposition of A. % % Parameters: % A has size [n, n] and rank(A) = n. % b has size [n, 1]. % % Returns: % x has size [n, 1].
Created Sep 1, 2015
View deriv_lsq_nonlin.m
 function dy_dx = deriv_lsq_nonlin(rfun, x, y_hat, m, n) % deriv_lsq_nonlin estimates the derivative of % y(x) = argmin_y |r(x, y)|^2 . % % Parameters: % [r, dr_dx, dr_dy, d2r_dxdy] = rfun(x, y) % x has size [m, 1]. % y has size [n, 1]. % r has size [p, 1]. % dr_dx has size [p, m] and dr_dx(i, j) = dr(i) / dx(j).
Last active Aug 29, 2015
Estimate RIP constant of matrix
View rip_svd.m
 n = 100; m = 50; A = 1/sqrt(m) * randn(m, n); K = 10; rmin = ones(K, 1); rmax = ones(K, 1); trials = 1e4; for k = 1:K for i = 1:trials
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