def test_relation_sparse_2d_dataview_digest(): row = np.array([0, 3, 1, 0]) col = np.array([0, 3, 1, 2]) data = np.array([4, 5, 7, 9]) m = coo_matrix((data, (row, col)), shape=(4, 4)) view = sparse_2d_dataview(m) view1 = sparse_2d_dataview(m) assert_equals(_hexdigest(view), _hexdigest(view1)) row1 = np.array([0, 3, 1, 0]) col1 = np.array([0, 3, 1, 2]) data1 = np.array([4, 5, 7, 1]) m1 = coo_matrix((data1, (row1, col1)), shape=(4, 4)) view = sparse_2d_dataview(m) view1 = sparse_2d_dataview(m1) assert_not_equals(_hexdigest(view), _hexdigest(view1))
def test_relation_sparse_2d_dataview_digest(): row = np.array([0, 3, 1, 0]) col = np.array([0, 3, 1, 2]) data = np.array([4, 5, 7, 9]) m = coo_matrix((data, (row, col)), shape=(4, 4)) view = sparse_2d_dataview(m) view1 = sparse_2d_dataview(m) assert_equals(_hexdigest(view), _hexdigest(view1)) row1 = np.array([0, 3, 1, 0]) col1 = np.array([0, 3, 1, 2]) data1 = np.array([4, 5, 7, 1]) m1 = coo_matrix((data1, (row1, col1)), shape=(4, 4)) view = sparse_2d_dataview(m) view1 = sparse_2d_dataview(m1) assert_not_equals(_hexdigest(view), _hexdigest(view1))
def test_relation_sparse_2d_dataview_build_from_csc(): row = np.array([0, 3, 1, 0]) col = np.array([0, 3, 1, 2]) data = np.array([4, 5, 7, 9]) m = coo_matrix((data, (row, col)), shape=(4, 4)) csc = m.tocsc() assert isinstance(csc, csc_matrix) view = sparse_2d_dataview(csc) assert view.shape() == (4, 4)
def test_relation_sparse_2d_dataview_build_from_csc(): row = np.array([0, 3, 1, 0]) col = np.array([0, 3, 1, 2]) data = np.array([4, 5, 7, 9]) m = coo_matrix((data, (row, col)), shape=(4, 4)) csc = m.tocsc() assert isinstance(csc, csc_matrix) view = sparse_2d_dataview(csc) assert view.shape() == (4, 4)
def test_relation_sparse_2d_dataview_pickle(): def sparsify(y, fn=lambda x: x): inds = it.product(range(y.shape[0]), range(y.shape[1])) ijv = [(i, j, y[i, j]) for i, j in inds if fn(y[i, j])] args = (map(op.itemgetter(2), ijv), (map(op.itemgetter(0), ijv), map(op.itemgetter(1), ijv))) return coo_matrix(args, shape=y.shape) y = np.random.randint(-2, 2, size=(10, 10)) view = sparse_2d_dataview(sparsify(y)) bstr = pickle.dumps(view) view1 = pickle.loads(bstr) assert_equals((view.tocsr() != view1.tocsr()).nnz, 0) y = np.random.uniform(size=(4, 3)) view = sparse_2d_dataview(sparsify(y, fn=lambda x: x >= 0.4 and x <= 0.6)) bstr = pickle.dumps(view) view1 = pickle.loads(bstr) assert_almost_equals(np.abs( (view.tocsr() - view1.tocsr()).todense()).max(), 0., places=3)
def test_relation_sparse_2d_dataview_pickle(): def sparsify(y, fn=lambda x: x): inds = it.product(range(y.shape[0]), range(y.shape[1])) ijv = [(i, j, y[i, j]) for i, j in inds if fn(y[i, j])] args = ( map(op.itemgetter(2), ijv), (map(op.itemgetter(0), ijv), map(op.itemgetter(1), ijv)) ) return coo_matrix(args, shape=y.shape) y = np.random.randint(-2, 2, size=(10, 10)) view = sparse_2d_dataview(sparsify(y)) bstr = pickle.dumps(view) view1 = pickle.loads(bstr) assert_equals((view.tocsr() != view1.tocsr()).nnz, 0) y = np.random.uniform(size=(4, 3)) view = sparse_2d_dataview(sparsify(y, fn=lambda x: x >= 0.4 and x <= 0.6)) bstr = pickle.dumps(view) view1 = pickle.loads(bstr) assert_almost_equals( np.abs((view.tocsr() - view1.tocsr()).todense()).max(), 0., places=3)