(self)
| 1253 | cov(x, rowvar=False, bias=True)) |
| 1254 | |
| 1255 | def test_1d_with_missing(self): |
| 1256 | # Test cov 1 1D variable w/missing values |
| 1257 | x = self.data |
| 1258 | x[-1] = masked |
| 1259 | x -= x.mean() |
| 1260 | nx = x.compressed() |
| 1261 | assert_almost_equal(np.cov(nx), cov(x)) |
| 1262 | assert_almost_equal(np.cov(nx, rowvar=False), cov(x, rowvar=False)) |
| 1263 | assert_almost_equal(np.cov(nx, rowvar=False, bias=True), |
| 1264 | cov(x, rowvar=False, bias=True)) |
| 1265 | # |
| 1266 | try: |
| 1267 | cov(x, allow_masked=False) |
| 1268 | except ValueError: |
| 1269 | pass |
| 1270 | # |
| 1271 | # 2 1D variables w/ missing values |
| 1272 | nx = x[1:-1] |
| 1273 | assert_almost_equal(np.cov(nx, nx[::-1]), cov(x, x[::-1])) |
| 1274 | assert_almost_equal(np.cov(nx, nx[::-1], rowvar=False), |
| 1275 | cov(x, x[::-1], rowvar=False)) |
| 1276 | assert_almost_equal(np.cov(nx, nx[::-1], rowvar=False, bias=True), |
| 1277 | cov(x, x[::-1], rowvar=False, bias=True)) |
| 1278 | |
| 1279 | def test_2d_with_missing(self): |
| 1280 | # Test cov on 2D variable w/ missing value |
nothing calls this directly
no test coverage detected