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Methods defined here:
- __getslice__(self, i, j)
- !!! Do NOT create a new Matrice; so destruction of the
original Matrice entails destruction of this result
(x.__getslice__(i,j) <==> x[i:j]).
- __init__(self, **kw)
- Keyword argument:
fic -- Build from filename fic.
- backward(self, g, lx)
- Build of data g and Lexique lx, using backward algorithm,
with the logarithm of the computed probabilities.
Numbers of lx must be between 65 and 90, or between 97 and 122.
Descriptors of the Matrice are set using ascii code:
65-->A, 90-->Z, 97-->a, 122-->z.
- compress(self, g, i=1, **kw)
- Build for data g, using windows of size i(=1).
Keyword argument:
f -- if equals 'm', compute the mean value, otherwise compute the sum.
- derivate(self, g)
- Build computing the differences (pos_i-pos_(i-1)) on data g.
- fb(self, g, lx)
- Build of data g and Lexique lx, using forward-backward
algorithm, with the logarithm of the computed probabilities. Numbers
of lx must be between 65 and 90, or between 97 and 122. Descriptors of
the Matrice are set using ascii code: 65-->A, 90-->Z, 97-->a, 122-->z.
- forward(self, g, lx)
- Build of data g and Lexique lx, using forward algorithm,
with the logarithm of the computed probabilities.
Numbers of lx must be between 65 and 90, or between 97 and 122.
Descriptors of the Matrice are set using ascii code:
65-->A, 90-->Z, 97-->a, 122-->z.
- generate(self, lg, st)
- Create an empty Matrice, with length lg and descriptors the
characters of st.
- integrate(self, g)
- Build computing the cumulated sums on data g, ie:
res[i]=\sum_{k=0}^i g[k].
- read_nf(self, nf)
- Build from filename nf.
- translate(self, g, l)
- On Lexique l, build from data g. The descriptors are set
using the descriptor numbers of l and ascii code (so the descriptors
numbers must be between 65 and 90, or 97 and 122), and the values at
each position are computed by the predictions of the corresponding
descriptors.
Methods inherited from _Matr:
- __add__(self, m)
- Return a Matrice = self + (Matrice m) if both Matrice fit
(x.__add__(m) <==> x+m).
- __iadd__(self, m)
- += (Matrice m) if Matrices fit (x.__iadd__(m) <==> x+=m).
- __isub__(self, m)
- -= (Matrice m) if Matrices fit (x.__isub__(m) <==> x-=m).
- __len__(self)
- Return length of the Matrice (x.__len__() <==> len(x)).
- __str__(self)
- (x.__str__()<==> str(x))
- __sub__(self, m)
- Return a Matrice = self - (Matrice m) if Matrices fit
(x.__sub__(m) <==> x-m).
- desc(self)
- Return the list of descriptors.
- g_val(self, v, c, i)
- Put value v at character c line i.
- line(self, i)
- Return a dictionnary {character: value of the character at
line i}.
- melange(self)
- Quasi-uniformly randomized using (len*(log(len)+1)/2)
transpositions.
- ndesc(self)
- Return number of descriptors.
- val(self, c, i)
- Return the value of character c at line i.
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