SoftMaxModel = class SoftMaxModel(NonLinearModel) |
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SoftMaxModel(ndim=1, ndout=1, copy=None, offset=False, normed=True, **kwargs)
Softmax Model is a Logistic model if the number of outputs is 1.
Otherwise it is generalization of the LogisticModel over multiple outputs
exp( sum_k( x_k * p_kn ) + q_n ) )
f_n( x:p ) = -------------------------------------------
sum_i( exp( sum_k( x_k * p_ki ) + q_i ) ) )
0 0 0 0 0 0 I inputs
|\ /|\ /|\ /|\ /|\ /|
all inputs connect to all outputs I*N connecting parameters
\|/ \|/ \|/ \|/ \|/ N offset parameters (if offset)
0 0 0 0 0 N outputs
The parameters (p) are initialized at 1.0, except the offset (q).
They are initialized at 0.0.
Attributes
----------
offset : bool
True : the outputs have offsets
ndout : int
number of output categories
in2out : int
ndim * ndout
normed : bool
the results are normalized (def:True)
Attributes from Model
---------------------
npchain, parameters, stdevs, xUnit, yUnit
Attributes from FixedModel
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npmax, fixed, parlist, mlist
Attributes from BaseModel
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npbase, ndim, priors, posIndex, nonZero,
tiny, deltaP, parNames |
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- Method resolution order:
- SoftMaxModel
- NonLinearModel
- Model
- FixedModel
- BaseModel
- builtins.object
Constructor:
- SoftMaxModel( ndim=1, ndout=1, copy=None, offset=False, normed=True, **kwargs )
- Logistic response model.
Number of parameters is npars (see offset)
Parameters
----------
ndim : int
number of inputs
ndout : int
number of classifications
offset : bool
False : no offsets npars = ndim * ndout
True : each output has one offset: npars = ndim * ndout + ndout
normed : bool
True : output is normalized
False : not
copy : SoftMaxModel
to be copied
Methods defined here:
- baseDerivative( xdata, params )
- Return the derivative df_i/dx_n of each output f_i to the data x_n
at each xdata (=x).
It is returned as an array of shape (N,I) of an array of length K.
N is #outputs; I is #inputs (ndim); K is #datapoints.
Parameters
----------
xdata : array_like
values at which to calculate the result
params : array_like
values for the parameters.
- baseName()
- Returns a string representation of the model.
- baseParameterUnit( k )
- Return the unit of the indicated parameter.
Parameters
---------
k : int
parameter number.
- basePartial( xdata, params, parlist=None )
- Returns the partials at the input value as a list (size N) of arrays
of shape (K,P). N is #outputs; K is #datapoints; P is #parameters.
Parameters
----------
xdata : array_like
values at which to calculate the partials
params : array_like
values for the parameters.
parlist : array_like
list of indices active parameters (or None for all)
- baseResult( xdata, params )
- Returns the result of the model function: F_n( x_k ) as array of
shape [nx,ndout], where nx number of data points and ndout is the number of
outputs.
Parameters
----------
xdata : array_like
values at which to calculate the result
params : array_like
values for the parameters.
- copy()
- Copy method.
Methods inherited from NonLinearModel:
Methods inherited from Model:
Overloaded operators and aliases
Other methods
- addModel( model )
- appendModel( model, operation )
- assignDF1( partial, i, dpi )
- assignDF2( partial, i, dpi )
- chainLength()
- correctParameters( params )
- derivative( xdata, param, useNum=False )
- divideModel( model )
- domain2Unit( dvalue, kpar=None )
- getIntegralUnit()
- getLimits()
- getLinearIndex()
- getNumberOfParameters()
- getParameterName( k )
- getParameterUnit( k )
- getPrior( k )
- hasLimits( fitindex=None )
- hasPriors( isBound=True )
- isDynamic()
- isNullModel()
- isolateModel( k )
- multiplyModel( model )
- nextPrior()
- numDerivative( xdata, param )
- numPartial( xdata, param )
- operate( res, pars, next )
- partialDomain2Unit( dvalue )
- pipeModel( model )
- pipe_0( dGd, dHdG)
- pipe_1( dGd, dHdG)
- pipe_2( dGd, dHdG)
- pipe_3( dGd, dHdG)
- pipe_4( dGdx, dHdG)
- pipe_5( dGdx, dHdG)
- pipe_6( dGdx, dHdG)
- pipe_7( dGdx, dHdG)
- pipe_8( dGdx, dHdG)
- pipe_9( dGdx, dHdG)
- result( xdata, param=None )
- selectPipe( ndim, ninter, ndout )
- setLimits( lowLimits=None, highLimits=None )
- setPrior( k, prior=None, **kwargs )
- shortName()
- strictNumericDerivative( xdata, param )
- strictNumericPartial( xdata, params, parlist=None )
- subtractModel( model )
- testPartial( xdata, params, silent=True )
- unit2Domain( uvalue, kpar=None )
Methods inherited from FixedModel:
Methods inherited from BaseModel:
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