Source code for feets.extractors.ext_beyond1_std
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# The MIT License (MIT)
# Copyright (c) 2017 Juan Cabral
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# =============================================================================
# FUTURE
# =============================================================================
from __future__ import unicode_literals
# =============================================================================
# DOC
# =============================================================================
__doc__ = """"""
# =============================================================================
# IMPORTS
# =============================================================================
import numpy as np
from .core import Extractor
# =============================================================================
# EXTRACTOR CLASS
# =============================================================================
[docs]class Beyond1Std(Extractor):
"""
**Beyond1Std**
Percentage of points beyond one standard deviation from the weighted mean.
For a normal distribution, it should take a value close to 0.32:
.. code-block:: pycon
>>> fs = feets.FeatureSpace(only=['Beyond1Std'])
>>> features, values = fs.extract(**lc_normal)
>>> dict(zip(features, values))
{'Beyond1Std': 0.317}
References
----------
.. [richards2011machine] Richards, J. W., Starr, D. L., Butler, N. R.,
Bloom, J. S., Brewer, J. M., Crellin-Quick, A., ... &
Rischard, M. (2011). On machine-learned classification of variable stars
with sparse and noisy time-series data.
The Astrophysical Journal, 733(1), 10. Doi:10.1088/0004-637X/733/1/10.
"""
data = ['magnitude', 'error']
features = ["Beyond1Std"]
[docs] def fit(self, magnitude, error):
n = len(magnitude)
weighted_mean = np.average(magnitude, weights=1 / error ** 2)
# Standard deviation with respect to the weighted mean
var = sum((magnitude - weighted_mean) ** 2)
std = np.sqrt((1.0 / (n - 1)) * var)
count = np.sum(np.logical_or(magnitude > weighted_mean + std,
magnitude < weighted_mean - std))
return {"Beyond1Std": float(count) / n}