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# scipy.maxentropy.conditionalmodel.setfeaturesandsamplespace

`conditionalmodel.``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.

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