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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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""" |
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A compressed sensing edition of The Cannon. |
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""" |
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from __future__ import (division, print_function, absolute_import, |
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unicode_literals) |
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__all__ = ["LassoCannonModel"] |
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import logging |
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import numpy as np |
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import scipy.optimize as op |
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from . import (model, utils) |
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logger = logging.getLogger(__name__) |
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class LassoCannonModel(model.BaseCannonModel): |
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""" |
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A compressed sensing edition of The Cannon model for the estimation of |
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arbitrary stellar labels with regularized complexity. |
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:param labels: |
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A table with columns as labels, and stars as rows. |
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:type labels: |
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:class:`~astropy.table.Table` or numpy structured array |
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:param fluxes: |
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An array of fluxes for stars in the training set, given as shape |
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`(num_stars, num_pixels)`. The `num_stars` should match the number of |
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rows in `labels`. |
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:type fluxes: |
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:class:`np.ndarray` |
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:param flux_uncertainties: |
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An array of 1-sigma flux uncertainties for stars in the training set, |
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The shape of the `flux_uncertainties` should match `fluxes`. |
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:type flux_uncertainties: |
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:class:`np.ndarray` |
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:param dispersion: [optional] |
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The dispersion values corresponding to the given pixels. If provided, |
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this should have length `num_pixels`. |
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:param live_dangerously: [optional] |
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If enabled then no checks will be made on the label names, prohibiting |
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the user to input human-readable forms of the label vector. |
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""" |
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_descriptive_attributes = ["_label_vector"] |
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_trained_attributes = ["_coefficients", "_scatter"] |
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_data_attributes = \ |
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["training_labels", "training_fluxes", "training_flux_uncertainties"] |
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def __init__(self, *args, **kwargs): |
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super(LassoCannonModel, self).__init__(*args, **kwargs) |
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# This class has additional attributes. |
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# label_complexity? |
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# regularization_behaviour? |
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# etc... |
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@model.requires_label_vector |
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def train(self, **kwargs): |
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raise NotImplementedError |
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@model.requires_training_wheels |
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def predict(self, labels=None, **labels_as_kwargs): |
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raise NotImplementedError |
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@model.requires_training_wheels |
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def fit(self, fluxes, flux_uncertainties, **kwargs): |
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raise NotImplementedError |
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andycasey /
AnniesLasso
