Class lls_base

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Instance Methods

__init__(self, a, copy=True)
x.__init__(...) initializes x; see help(type(x)) for signature

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set_y(self, y, copy=True)

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y(self, copy=True)

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hat(self, copy=True)

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x(self, y=None, copy=True)

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fit(self, copy=False)

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residual(self)

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variance_about_fit(self)
Returns the estimator of the standard deviation of the data about the fit.

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float

eff_n(self)
Returns something like the number of data, except that it looks at their weighting and the structure of the problem.

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float

eff_rank(self)
Returns something like the rank of the solution, but rather than counting how many dimensions can be solved at all, it reports how many dimensions can be solved with a relatively good accuracy.

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Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Properties
Inherited from `object`: `__class__`

Method Details

init(self, a, copy=True)
(Constructor)

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x.__init__(...) initializes x; see help(type(x)) for signature

Overrides: object.__init__: (inherited documentation)

x(self, y=None, copy=True)

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Raises:

numpy.linalg.linalg.LinAlgError - when the matrix is singular.

fit(self, copy=False)

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Raises:

numpy.linalg.linalg.LinAlgError - when the matrix is singular.

variance_about_fit(self)

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Returns the estimator of the standard deviation of the data about the fit.

Returns:: numpy.ndarray with shape=(q,). Each entry corresponds to one of the q sets of equations that are being fit.

eff_n(self)

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Returns something like the number of data, except that it looks at their weighting and the structure of the problem. It counts how many data have a relatively large effect on the solution, and if a datum has little influence, it doesn't count for much.

Returns: float

Class lls_base

__init__(self, a, copy=True) (Constructor)

x(self, y=None, copy=True)

fit(self, copy=False)

variance_about_fit(self)

eff_n(self)

init(self, a, copy=True)
(Constructor)