scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
setfeaturesandsamplespace(f, samplespace)
Creates a new matrix self.F of features f of all points in the sample space. f is a list of feature functions f_i
mapping the sample space to real values. The parameter vector self.params is initialized to zero.
We also compute f(x) for each x in the sample space and store them as self.F. This uses lots of memory but is
much faster.
This is only appropriate when the sample space is finite.
class bigmodel()
A maximum-entropy (exponential-form) model on a large sample space.
The model expectations are not computed exactly (by summing or integrating over a sample space) but approximately
(by Monte Carlo estimation). Approximation is necessary when the sample space is too large to sum or
integrate over in practice, like a continuous sample space in more than about 4 dimensions or a large discrete
space like all possible sentences in a natural language.
Approximating the expectations by sampling requires an instrumental distribution that should be close to the
model for fast convergence. The tails should be fatter than the model.
252 Chapter 3. Reference