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Package crino :: Module criterion :: Class MeanSquareError
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Class MeanSquareError

source code

The mean square error criterion is used in the least squares method, it is well suited for data fitting.

The mean square loss can be written as follows :

LMSE =  − 1 ⁄ N ∑Nk = 1(y − ŷ)2

Instance Methods [hide private]
 
__init__(self, outputs, targets)
Constructs a new MeanSquareError criterion.		 source code
 
prepare(self)
Computes the mean square error symbolic expression.		 source code
Instance Variables [hide private]

Inherited from Criterion: expression, outputs, targets

Method Details [hide private]

__init__(self, outputs, targets)
(Constructor)

source code 
Constructs a new MeanSquareError criterion.
Parameters:
Overrides: Criterion.__init__

prepare(self)

source code 
Computes the mean square error symbolic expression.
Overrides: Criterion.prepare

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