Esempio n. 1
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def AR1(constrained=False):
    g = .95
    sn = .3
    y, c, s = [a[0] for a in gen_data([g], sn, N=1)]
    result = constrained_oasisAR1(y, g, sn) if constrained else oasisAR1(y, g, lam=2.4)
    result_foopsi = constrained_foopsi(y, [g], sn) if constrained else foopsi(y, [g], lam=2.4)
    npt.assert_allclose(np.corrcoef(result[0], result_foopsi[0])[0, 1], 1)
    npt.assert_allclose(np.corrcoef(result[1], result_foopsi[1])[0, 1], 1)
    npt.assert_allclose(np.corrcoef(result[0], c)[0, 1], 1, .03)
    npt.assert_allclose(np.corrcoef(result[1], s)[0, 1], 1, .2)
Esempio n. 2
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def AR2(constrained=False):
    g = [1.7, -.712]
    sn = .3
    y, c, s = [a[0] for a in gen_data(g, sn, N=1, seed=3)]
    result = constrained_onnlsAR2(y, g, sn) if constrained else onnls(y, g, lam=25)
    result_foopsi = constrained_foopsi(y, g, sn) if constrained else foopsi(y, g, lam=25)
    npt.assert_allclose(np.corrcoef(result[0], result_foopsi[0])[0, 1], 1, 1e-3)
    npt.assert_allclose(np.corrcoef(result[1], result_foopsi[1])[0, 1], 1, 1e-2)
    npt.assert_allclose(np.corrcoef(result[0], c)[0, 1], 1, .03)
    npt.assert_allclose(np.corrcoef(result[1], s)[0, 1], 1, .2)
    result2 = constrained_oasisAR2(y, g[0], g[1], sn) if constrained \
        else oasisAR2(y, g[0], g[1], lam=25)
    npt.assert_allclose(np.corrcoef(result2[0], c)[0, 1], 1, .03)
    npt.assert_allclose(np.corrcoef(result2[1], s)[0, 1], 1, .2)
Esempio n. 3
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                print("The solver " + solver +
                      " is actually not installed, hence skipping it.")
                break
constrained_ts = {}
for solver in solvers[:-1]:  # GUROBI failed
    constrained_ts[solver] = np.nan * np.zeros(N)
    print(
        'running %7s with p=1 and optimizing lambda such that noise constraint is tight'
        % solver)
    for i, y in enumerate(Y):
        if solver == 'OASIS':
            constrained_ts[solver][i] = Timer(lambda: constrained_oasisAR1(
                y, g=g, sn=sn)).timeit(number=runs) / runs
        else:
            try:
                constrained_ts[solver][i] = Timer(lambda: constrained_foopsi(
                    y, g=[g], sn=sn, solver=solver)).timeit(number=runs) / runs
            except SolverError:
                print("The solver " + solver +
                      " is actually not installed, hence skipping it.")
                break
constrained_ts['GUROBI'] = np.zeros(N) * np.nan  # GUROBI failed

# plot
fig = plt.figure(figsize=(7, 5))
fig.add_axes([.14, .17, .79, .82])
plt.errorbar(range(len(solvers)), [np.mean(ts[s]) for s in solvers],
             [np.std(ts[s]) / np.sqrt(N) for s in solvers],
             ls='',
             marker='o',
             ms=10,
             c=col[0],
Esempio n. 4
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    plt.ylim(-.2, 1.1)
    plt.xlim(0, 452)
    plt.ylabel(r'$s_{\min}$')
    plt.xlabel('Time [s]', labelpad=-10)
    plt.show()


# AR(1)

g = .95
sn = .3
Y, trueC, trueSpikes = gen_data()
y = Y[0]
N, T = Y.shape

c, s = constrained_foopsi(y, [g], sn)[:2]
c_t, s_t = oasisAR1(y, g, s_min=.55)
res = [np.where(oasisAR1(y, g, s_min=s0)[1] > 1e-2)[0]
       for s0 in np.arange(0, 1.1, .1)]
plotTrace(True)


# AR(2)

g = [1.7, -.712]
sn = 1.
Y, trueC, trueSpikes = gen_data(g, sn, seed=3)
rng = slice(150, 600)
trueC = trueC[:, rng]
trueSpikes = trueSpikes[:, rng]
y = Y[0, rng]