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The algorithm would find its place in one of the existing packages (where ''optim'' or ''signal'' sound appropriate) or as a separate package. I plan to prepare two versions of the general algorithm, (temporal name {{codeline|maxent}}) | The algorithm would find its place in one of the existing packages (where ''optim'' or ''signal'' sound appropriate) or as a separate package. I plan to prepare two versions of the general algorithm, (temporal name {{codeline|maxent}}) | ||
* a version for problems defined by matrix. The function's declaration should be something like this | * a version for problems defined by matrix. The function's declaration should be something like this | ||
{{ | {{Codeline|[x,info,...]=maxent(y,D,sigma,alpha=0.95, model=1, optset}} | ||
where {{codeline|y} is the data vector, and {{codeline|D}} is the transformation matrix. {{codeline|sigma}} should be a vector or scalar which describes standard deviation of values of {{codeline|y}}. The optional parameter {{codeline|alpha}} and {{codeline|model}} describe confidence and a priori distribution of {{codeline|x}} (defaults to flat) respectively. The last parameter {{codeline|optset}} would allow to pass additional parameters to function, similar to the ones in {{codeline|optim}} package. | where {{codeline|y} is the data vector, and {{codeline|D}} is the transformation matrix. {{codeline|sigma}} should be a vector or scalar which describes standard deviation of values of {{codeline|y}}. The optional parameter {{codeline|alpha}} and {{codeline|model}} describe confidence and a priori distribution of {{codeline|x}} (defaults to flat) respectively. The last parameter {{codeline|optset}} would allow to pass additional parameters to function, similar to the ones in {{codeline|optim}} package. | ||
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* another version would be defined for a non-linear function. The declaration would very similar | * another version would be defined for a non-linear function. The declaration would very similar | ||
{{ | {{Codeline|[x,info,...]=maxent(y,fun,sigma,alpha=0.95, model=1, optset}}. | ||
[x,info,...]=maxent(y,fun,sigma,alpha=0.95, model=1, optset | |||
}}. | |||
All the parameters have the similar meaning, and the new parameter {{codeline|fun}} is the handle to a function which accepts vector argument, which describes the problem. This time the returned value should obey <math> y \approx f(x)</math> | All the parameters have the similar meaning, and the new parameter {{codeline|fun}} is the handle to a function which accepts vector argument, which describes the problem. This time the returned value should obey <math> y \approx f(x)</math> |
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