# Sample data from a sparse multivariate normal

dim = 10 # 80

n_samples = 60

# Generate input data

random_state = check_random_state(random_state_seed)

prec = make_sparse_spd_matrix(dim, alpha=.96, random_state=random_state)

cov = linalg.inv(prec)

X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)

utils.cache_value(X, "prof_graph_lasso_cv/X_%d_%d_%d" %

(dim, n_samples, random_state_seed))

# Test with alphas as integer

## mode = 'cd'

## gl1 = utils.timeit(GraphLassoCV(verbose=1, alphas=3, mode=mode).fit)(X)

## utils.cache_value(gl1.covariance_,

## "prof_graph_lasso_cv/covariance_%d_%d_%d" %

## (dim, n_samples, random_state_seed))

## utils.cache_value(gl1.precision_,

## "prof_graph_lasso_cv/precision_%d_%d_%d" %

## (dim, n_samples, random_state_seed))

# Test with alphas as list.

# Take same alphas as were found in the first step, check the result

# is the same.

## gl2 = utils.timeit(GraphLassoCV(alphas=gl1.cv_alphas_, n_jobs=1,

## mode=mode).fit)(X)

## np.testing.assert_almost_equal(gl1.covariance_, gl2.covariance_,

## decimal=3)

## np.testing.assert_almost_equal(gl1.precision_, gl2.precision_,

## decimal=3)

## np.testing.assert_almost_equal(gl1.alpha_, gl2.alpha_)

# Smoke test with an alternate cross-validation object.

gl3 = utils.timeit(GraphLassoCV(cv=KFold(n=X.shape[0], n_folds=20),

n_jobs=1).fit)(X)

## utils.cache_value(gl3.covariance_,

## "prof_graph_lasso_cv/gl3_covariance_%d_%d_%d" %

## (dim, n_samples, random_state_seed))

## utils.cache_value(gl3.precision_,

## "prof_graph_lasso_cv/gl3_precision_%d_%d_%d" %