def test_splinelg_mle_small_rf(): w_true, X, y, dims, dt = generate_2d_rf_data(noise='white') df = [3, 4] model = splineLG(X, y, dims=dims, dt=dt, df=df, compute_mle=True) assert mse(uvec(model.w_mle), uvec(w_true.flatten())) < 1e-1
def test_splinelnln_small_rf(): w_true, X, y, dims, dt = generate_2d_rf_data(noise='white') model = LNLN(X, y, dims=dims, dt=dt) model.fit(metric='corrcoef', num_iters=100, verbose=0, tolerance=10, beta=0.01) assert model.w_opt is not None
def test_splinelg_small_rf(): w_true, X, y, dims, dt = generate_2d_rf_data(noise='white') df = [3, 4] model = splineLG(X, y, dims=dims, dt=dt, df=df) model.fit(metric='corrcoef', num_iters=100, verbose=0, tolerance=10, beta=0.01) assert mse(uvec(model.w_opt), uvec(w_true.flatten())) < 1e-1
def test_asd_small_rf(): w_true, X, y, dims, dt = generate_2d_rf_data(noise='white') model = ASD(X, y, dims=dims) model.fit(p0=[ 1., 1., 6., 6., ], num_iters=10, verbose=10) w_fit = model.optimized_C_post @ X.T @ y / model.optimized_params[0]**2 assert mse(uvec(w_fit), uvec(w_true.flatten())) < 1e-1
def test_ald_small_rf(): w_true, X, y, dims, dt = generate_2d_rf_data(noise='white') sigma0 = [1.3] rho0 = [0.8] params_t0 = [3., 20., 3., 20.9] # taus, nus, tauf, nuf params_y0 = [3., 20., 3., 20.9] p0 = sigma0 + rho0 + params_t0 + params_y0 model = ALD(X, y, dims=dims) model.fit(p0=p0, num_iters=30, verbose=10) w_fit = model.optimized_C_post @ X.T @ y / model.optimized_params[0]**2 assert mse(uvec(w_fit), uvec(w_true.flatten())) < 1e-1
def test_splinelnln_mle_small_rf(): w_true, X, y, dims, dt = generate_2d_rf_data(noise='white') model = LNLN(X, y, dims=dims, dt=dt, compute_mle=True) assert model.w_mle is not None
def test_lnp_mle_small_rf(): w_true, X, y, dims, dt = generate_2d_rf_data(noise='white') model = LNP(X, y, dims=dims, dt=dt, compute_mle=True) assert mse(uvec(model.w_mle), uvec(w_true.flatten())) < 1e-1