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Made the spec more clear.
--HG-- extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403343
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@ -173,10 +173,10 @@ namespace dlib
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- takes the given sample and projects it into the kernel feature space
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of out_vector_size() dimensions defined by this kernel map and
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returns the resulting vector.
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- in more precise terms, this function returns a vector V such that:
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- V.size() == out_vector_size()
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- in more precise terms, this function returns a vector such that:
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- The returned vector will contain out_vector_size() elements.
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- for any sample_type object S, the following equality is approximately true:
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- get_kernel()(sample,S) == dot(V, project(S)).
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- get_kernel()(sample,S) == dot(project(sample), project(S)).
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- The approximation error in the above equality will be zero (within rounding error)
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if both sample_type objects involved are within the span of the set of basis
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samples given to the load() function. If they are not then there will be some
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@ -226,11 +226,10 @@ namespace dlib
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(i.e. the returned distance function computes distances, in kernel feature space,
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between vect and any argument you give it. )
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- The approximation error in the above equality will be zero (within rounding error)
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if both sample_type objects involved are within the span of the set of basis
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samples given to the load() function. If they are not then there will be some
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approximation error. Note that all the basis samples are always within their
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own span. So the equality is always exact for the samples given to the load()
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function.
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if S is within the span of the set of basis samples given to the load() function.
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If it is not then there will be some approximation error. Note that all the basis
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samples are always within their own span. So the equality is always exact for the
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samples given to the load() function.
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- DF.kernel_function == get_kernel()
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- DF.b == dot(vect,vect)
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- DF.basis_vectors == these will be the basis samples given to the previous call to load(). Note
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