def test_estimator():
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
omp.fit(X, y[:, 0])
assert omp.coef_.shape == (n_features, )
assert omp.intercept_.shape == ()
assert np.count_nonzero(omp.coef_) <= n_nonzero_coefs
omp.fit(X, y)
assert omp.coef_.shape == (n_targets, n_features)
assert omp.intercept_.shape == (n_targets, )
assert np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs
coef_normalized = omp.coef_[0].copy()
omp.set_params(fit_intercept=True, normalize=False)
omp.fit(X, y[:, 0])
assert_array_almost_equal(coef_normalized, omp.coef_)
omp.set_params(fit_intercept=False, normalize=False)
omp.fit(X, y[:, 0])
assert np.count_nonzero(omp.coef_) <= n_nonzero_coefs
assert omp.coef_.shape == (n_features, )
assert omp.intercept_ == 0
omp.fit(X, y)
assert omp.coef_.shape == (n_targets, n_features)
assert omp.intercept_ == 0
assert np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs
def test_estimator():
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
omp.fit(X, y[:, 0])
assert_equal(omp.coef_.shape, (n_features,))
assert_equal(omp.intercept_.shape, ())
assert np.count_nonzero(omp.coef_) <= n_nonzero_coefs
omp.fit(X, y)
assert_equal(omp.coef_.shape, (n_targets, n_features))
assert_equal(omp.intercept_.shape, (n_targets,))
assert np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs
coef_normalized = omp.coef_[0].copy()
omp.set_params(fit_intercept=True, normalize=False)
omp.fit(X, y[:, 0])
assert_array_almost_equal(coef_normalized, omp.coef_)
omp.set_params(fit_intercept=False, normalize=False)
omp.fit(X, y[:, 0])
assert np.count_nonzero(omp.coef_) <= n_nonzero_coefs
assert_equal(omp.coef_.shape, (n_features,))
assert_equal(omp.intercept_, 0)
omp.fit(X, y)
assert_equal(omp.coef_.shape, (n_targets, n_features))
assert_equal(omp.intercept_, 0)
assert np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs
def test_estimator():
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
omp.fit(X, y[:, 0])
assert_equal(omp.coef_.shape, (n_features, ))
assert_equal(omp.intercept_.shape, ())
assert_true(count_nonzero(omp.coef_) <= n_nonzero_coefs)
omp.fit(X, y)
assert_equal(omp.coef_.shape, (n_targets, n_features))
assert_equal(omp.intercept_.shape, (n_targets, ))
assert_true(count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs)
omp.set_params(fit_intercept=False, normalize=False)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
omp.fit(X, y[:, 0], Gram=G, Xy=Xy[:, 0])
assert_equal(omp.coef_.shape, (n_features, ))
assert_equal(omp.intercept_, 0)
assert_true(count_nonzero(omp.coef_) <= n_nonzero_coefs)
assert_true(len(w) == 2)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
omp.fit(X, y, Gram=G, Xy=Xy)
assert_equal(omp.coef_.shape, (n_targets, n_features))
assert_equal(omp.intercept_, 0)
assert_true(count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs)
assert_true(len(w) == 2)
def test_estimator():
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
omp.fit(X, y[:, 0])
assert_equal(omp.coef_.shape, (n_features,))
assert_equal(omp.intercept_.shape, ())
assert_true(np.count_nonzero(omp.coef_) <= n_nonzero_coefs)
omp.fit(X, y)
assert_equal(omp.coef_.shape, (n_targets, n_features))
assert_equal(omp.intercept_.shape, (n_targets,))
assert_true(np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs)
omp.set_params(fit_intercept=False, normalize=False)
omp.fit(X, y[:, 0])
assert_equal(omp.coef_.shape, (n_features,))
assert_equal(omp.intercept_, 0)
assert_true(np.count_nonzero(omp.coef_) <= n_nonzero_coefs)
omp.fit(X, y)
assert_equal(omp.coef_.shape, (n_targets, n_features))
assert_equal(omp.intercept_, 0)
assert_true(np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs)
def test_estimator():
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
omp.fit(X, y[:, 0])
assert_equal(omp.coef_.shape, (n_features,))
assert_equal(omp.intercept_.shape, ())
assert_true(count_nonzero(omp.coef_) <= n_nonzero_coefs)
omp.fit(X, y)
assert_equal(omp.coef_.shape, (n_targets, n_features))
assert_equal(omp.intercept_.shape, (n_targets,))
assert_true(count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs)
omp.set_params(fit_intercept=False, normalize=False)
assert_warns(DeprecationWarning, omp.fit, X, y[:, 0], Gram=G, Xy=Xy[:, 0])
assert_equal(omp.coef_.shape, (n_features,))
assert_equal(omp.intercept_, 0)
assert_true(count_nonzero(omp.coef_) <= n_nonzero_coefs)
assert_warns(DeprecationWarning, omp.fit, X, y, Gram=G, Xy=Xy)
assert_equal(omp.coef_.shape, (n_targets, n_features))
assert_equal(omp.intercept_, 0)
assert_true(count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs)
def test_estimator():
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
omp.fit(X, y[:, 0])
assert_equal(omp.coef_.shape, (n_features, ))
assert_equal(omp.intercept_.shape, ())
assert_true(np.count_nonzero(omp.coef_) <= n_nonzero_coefs)
omp.fit(X, y)
assert_equal(omp.coef_.shape, (n_targets, n_features))
assert_equal(omp.intercept_.shape, (n_targets, ))
assert_true(np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs)
omp.set_params(fit_intercept=False, normalize=False)
omp.fit(X, y[:, 0])
assert_equal(omp.coef_.shape, (n_features, ))
assert_equal(omp.intercept_, 0)
assert_true(np.count_nonzero(omp.coef_) <= n_nonzero_coefs)
omp.fit(X, y)
assert_equal(omp.coef_.shape, (n_targets, n_features))
assert_equal(omp.intercept_, 0)
assert_true(np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs)