Timings, before: >>> x = np.linspace(-1000, 1000, 10000) >>> %timeit logistic_sigmoid(x) 1000 loops, best of 3: 570 us per loop After: >>> %timeit logistic_sigmoid(x) 1000 loops, best of 3: 286 us per loop For comparison: >>> %timeit np.tanh(x) 10000 loops, best of 3: 117 us per loop >>> %timeit .5 * (1 + np.tanh(x/2.)) 1000 loops, best of 3: 301 us per loop TODO: optimize log_logistic_sigmoid. |
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| .. | ||
| __init__.py | ||
| test_bench.py | ||
| test_class_weight.py | ||
| test_extmath.py | ||
| test_fixes.py | ||
| test_graph.py | ||
| test_linear_assignment.py | ||
| test_multiclass.py | ||
| test_murmurhash.py | ||
| test_random.py | ||
| test_shortest_path.py | ||
| test_sparsefuncs.py | ||
| test_testing.py | ||
| test_utils.py | ||
| test_validation.py | ||