(f, bounded=False)
| 26 | |
| 27 | |
| 28 | def params_1(f, bounded=False): |
| 29 | a = 5.0 |
| 30 | b = np.arange(2.0, 12.0) |
| 31 | c = np.arange(2.0, 102.0).reshape((10, 10)) |
| 32 | d = np.arange(2.0, 1002.0).reshape((10, 10, 10)) |
| 33 | e = np.array([2.0, 3.0]) |
| 34 | g = np.arange(2.0, 12.0).reshape((1, 10, 1)) |
| 35 | if bounded: |
| 36 | a = 0.5 |
| 37 | b = b / (1.5 * b.max()) |
| 38 | c = c / (1.5 * c.max()) |
| 39 | d = d / (1.5 * d.max()) |
| 40 | e = e / (1.5 * e.max()) |
| 41 | g = g / (1.5 * g.max()) |
| 42 | |
| 43 | # Scalar |
| 44 | f(a) |
| 45 | # Scalar - size |
| 46 | f(a, size=(10, 10)) |
| 47 | # 1d |
| 48 | f(b) |
| 49 | # 2d |
| 50 | f(c) |
| 51 | # 3d |
| 52 | f(d) |
| 53 | # 1d size |
| 54 | f(b, size=10) |
| 55 | # 2d - size - broadcast |
| 56 | f(e, size=(10, 2)) |
| 57 | # 3d - size |
| 58 | f(g, size=(10, 10, 10)) |
| 59 | |
| 60 | |
| 61 | def comp_state(state1, state2): |
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