def random(cls, Alphabet, diags=None):
"""Makes random Q-matrix with specified diag elements and size.
diags can be a single float, or vector of values with same number
of chars as individual alphabet (e.g. list of 4 elements will act
as elements for the 4 bases).
"""
shape = Alphabet.Shape
single_size = shape[0]
if diags is None:
diags = -randarray(single_size)
else:
diags = array(diags, 'd')
#handle scalar case
if not diags.shape:
diags = reshape(diags, (1,))
if len(diags) == 1:
diags = repeat(diags, single_size)
temp = randarray((single_size, single_size-1))
temp *= ((-diags)/sum(temp, 1))[:,NewAxis]
result = diag(diags)
for r, row in enumerate(temp):
result[r][:r] = row[:r]
result[r][r+1:] = row[r:]
return cls(result, Alphabet)
def random(cls, Alphabet, diags=None):
"""Makes random Q-matrix with specified diag elements and size.
diags can be a single float, or vector of values with same number
of chars as individual alphabet (e.g. list of 4 elements will act
as elements for the 4 bases).
"""
shape = Alphabet.Shape
single_size = shape[0]
if diags is None:
diags = -randarray(single_size)
else:
diags = array(diags, 'd')
#handle scalar case
if not diags.shape:
diags = reshape(diags, (1, ))
if len(diags) == 1:
diags = repeat(diags, single_size)
temp = randarray((single_size, single_size - 1))
temp *= ((-diags) / sum(temp, 1))[:, NewAxis]
result = diag(diags)
for r, row in enumerate(temp):
result[r][:r] = row[:r]
result[r][r + 1:] = row[r:]
return cls(result, Alphabet)
def mutate(self, seq, random_vector=None):
"""Returns mutated version of seq, according to self.
seq should behave like a Numeric array.
random_vector should be vector of 0 and 1 of same length as sequence,
if supplied.
Result is always an array, not coerced into seq's class.
"""
sums = cumsum(self._data, 1)
model = take(sums, seq, axis=0)
if random_vector is None:
random_vector = randarray(seq.shape)
return sum(transpose(model)[:-1] < random_vector, axis=0)
def mutate(self, seq, random_vector=None):
"""Returns mutated version of seq, according to self.
seq should behave like a Numeric array.
random_vector should be vector of 0 and 1 of same length as sequence,
if supplied.
Result is always an array, not coerced into seq's class.
"""
sums = cumsum(self._data, 1)
model = take(sums, seq, axis=0)
if random_vector is None:
random_vector = randarray(seq.shape)
return sum(transpose(model)[:-1] < random_vector, axis=0)
def randomIndices(self, length, random_vector=None):
"""Produces random indices according to symbol freqs."""
freqs = cumsum(self._data/sum(self._data))[:-1]
if random_vector is None:
random_vector=randarray(length)
return searchsorted(freqs, random_vector)
def randomIndices(self, length, random_vector=None):
"""Produces random indices according to symbol freqs."""
freqs = cumsum(self._data / sum(self._data))[:-1]
if random_vector is None:
random_vector = randarray(length)
return searchsorted(freqs, random_vector)
