Add features #32
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Add features #32
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hombit
reviewed
Nov 2, 2020
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Please run |
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hombit
requested changes
Nov 5, 2020
| m_std = np.std(m, ddof=1) | ||
| m_new = np.cumsum(m - m_mean) | ||
| result = m_new / (len(m) * m_std) | ||
| return max(result) - min(result) |
| class Eta(BaseFeature): | ||
| def __call__(self, t, m, sigma=None, sorted=None, fill_value=None): | ||
| n = len(m) | ||
| m_std = np.std(m, ddof=1) ** 2 |
| def __call__(self, t, m, sigma=None, sorted=None, fill_value=None): | ||
| n = len(m) | ||
| m_std = np.std(m, ddof=1) ** 2 | ||
| m_sum = sum([(m[i + 1] - m[i]) ** 2 for i in range(n - 1)]) |
| def __call__(self, t, m, sigma=None, sorted=None, fill_value=None): | ||
| m_mean = np.mean(m) | ||
| d_mean = np.mean(np.power(sigma, 2)) | ||
| m_std = np.std(m, ddof=1) |
| def __call__(self, t, m, sigma=None, sorted=None, fill_value=None): | ||
| n = len(m) | ||
| m_mean = np.mean(m) | ||
| m_st = np.std(m, ddof=1) ** 4 |
| from ._base import BaseFeature | ||
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| class Kurtosis(BaseFeature): |
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| m_span = [m[i + 1] - m[i] for i in range(len(m) - 1)] | ||
| t_span = [t[i + 1] - t[i] for i in range(len(t) - 1)] | ||
| div = [abs(i / j) for i, j in zip(m_span, t_span)] |
| from ._base import BaseFeature | ||
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| class Skew(BaseFeature): |
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| class WeightedMean(BaseFeature): | ||
| def __call__(self, t, m, sigma=None, sorted=None, fill_value=None): | ||
| return np.average(m, weights=np.power(sigma, 2)) |
|
Check style test failed, can you, please, |
hombit
requested changes
Nov 13, 2020
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Probably some random-number statistics-motivated tests can be added. Thus, we can test AndersonDarlingNormal using a sample of normal distribution, and test ReducedChi2 with a random vector generated from multidimensional normal distribution having mu = [x, x, ..., x] and Sigma = diag(sigma_1, sigma_2, ...., sigma_n)
| n = len(m) | ||
| m_wmean = np.average(m, weights=sigma) | ||
| s = ((m - m_wmean) / sigma) ** 2 | ||
| return sum(s) / (n - 1) |
There was a problem hiding this comment.
np.sum is faster, proof:
In [1]: a = np.arange(1024)
In [2]: %timeit sum(a)
275 µs ± 22.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [3]: %timeit np.sum(a)
6.21 µs ± 548 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)| feature = ReducedChi2() | ||
| desired = feature(m, m, sigma) | ||
| actual = 10.666667 | ||
| assert_allclose(actual, desired) |
There was a problem hiding this comment.
Please add an additional test with non-constant sigma
hombit
reviewed
Nov 13, 2020
| class ReducedChi2(BaseFeature): | ||
| def __call__(self, t, m, sigma=None, sorted=None, fill_value=None): | ||
| n = len(m) | ||
| m_wmean = np.average(m, weights=sigma) |
add feature_tests.ipynb
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New features + tests