Class SparsePLSR
source code
object --+
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bases.BaseAlgorithm --+
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bases.ImplicitAlgorithm --+
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object --+ |
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bases.IterativeAlgorithm --+
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SparsePLSR
A NIPALS implementation for Sparse PLS regresison.
Parameters
----------
l : List or tuple of two non-negative floats. The Lagrange multipliers, or
regularisation constants, for the X and Y blocks, respectively.
penalise_y : Bool. Whether or not to penalise the Y block as well.
max_iter : Non-negative integer. Maximum allowed number of iterations.
Default is 200.
eps : Positive float. The tolerance used in the stopping criterion.
Examples
--------
>>> from parsimony.algorithms.nipals import SparsePLSR
>>> import numpy as np
>>> np.random.seed(42)
>>>
>>> X = np.random.rand(10, 10)
>>> Y = np.random.rand(10, 5)
>>> w = np.random.rand(10, 1)
>>> c = np.random.rand(5, 1)
>>> plsr = SparsePLSR(l=[4.0, 5.0])
>>> w, c = plsr.run([X, Y], [w, c])
>>> w
array([[ 0.32012726],
[ 0.31873833],
[ 0.15539258],
[ 0.64271827],
[ 0.23337738],
[ 0.54819589],
[ 0. ],
[ 0.06088551],
[ 0. ],
[ 0. ]])
>>> c
array([[ 0.1463623 ],
[ 0.66483154],
[ 0.4666803 ],
[ 0. ],
[ 0.5646119 ]])
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__init__(self,
l=[0.0, 0.0],
penalise_y=True,
max_iter=200,
eps=5e-08,
**kwargs)
x.__init__(...) initializes x; see help(type(x)) for signature |
source code
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run(self,
XY,
wc=None)
A NIPALS implementation for sparse PLS regresison. |
source code
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Inherited from bases.BaseAlgorithm:
get_params,
set_params
Inherited from bases.IterativeAlgorithm:
iter_reset
Inherited from object:
__delattr__,
__format__,
__getattribute__,
__hash__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
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Inherited from object:
__class__
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__init__(self,
l=[0.0, 0.0],
penalise_y=True,
max_iter=200,
eps=5e-08,
**kwargs)
(Constructor)
| source code
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x.__init__(...) initializes x; see help(type(x)) for signature
- Overrides:
object.__init__
- (inherited documentation)
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A NIPALS implementation for sparse PLS regresison.
Parameters
----------
XY : List of two numpy arrays. XY[0] is n-by-p and XY[1] is n-by-q. The
independent and dependent variables.
wc : List of numpy array. The start vectors.
Returns
-------
w : Numpy array, p-by-1. The weight vector of X.
c : Numpy array, q-by-1. The weight vector of Y.
- Overrides:
bases.ImplicitAlgorithm.run
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