def _normalize_init(self, init):
if init is None:
init = self.get_random_prestate()
elif self.order == 1 and not isiterable(init):
init = [init]
elif self.order > 1 and not isiterable(init):
init = self.get_random_prestate_startingwith([init])
elif isiterable(init) and self.order > len(init):
init = self.get_random_prestate_startingwith(list(init))
return init
def EXCLUDE_FUNC(prestate, poststate):
if isiterable(prestate):
intersection = EXCLUDED_STATES.intersection(set(prestate))
if intersection:
return True
return False
return prestate in EXCLUDED_STATES
def _aslist(x: t.Iter) -> t.List:
if isinstance(x, list):
return x
elif isiterable(x):
return list(x)
else:
raise TypeError("x should be iterable")
def windowed_moving_average(sequence, windowSize, weights=1, complete=True):
"""
return the weighted moving average of sequence with a
window size = windowSize.
weights can be a tuple defining the weighting in the window
for instance if windowSize = 5, weights could be (0.1, 0.5, 1, 0.5, 0.1)
if only one number is defined, a tupled is generated following a
interpol.halfcos curve
"""
import interpoltools
import numpy
def parse_coefs(n, coef):
# Asume que coef es un numero , definiendo una curva
n = n - 1 + n % 2
if n == 3:
return numpy.array((1., coef, 1.), 'd')
middle = int(n / 2)
coefs = []
for i in range(middle):
coefs.append(interpoltools.interpol_halfcos(i, 0, 1, middle, coef))
for i in range(middle, n):
coefs.append(interpoltools.interpol_halfcos(i, middle, coef, n - 1, 1))
return numpy.array(coefs)
l = len(sequence)
if not isiterable(weights):
weights = parse_coefs(windowSize, weights)
seq = numpy.array([sequence[i:l - (windowSize - i) + 1]
for i in range(windowSize)], 'd').transpose()
return (seq * weights).sum(1) / sum(weights)
def solve(solver: Solver,
numslots: t.Union[int, List[int]],
rater: Rater = None,
report=False,
reportMaxRows=10) -> List[Solution]:
"""
numslots: the number of slots to use, or a list of possible numslots
Example
~~~~~~~
values = [0.5, 1, 1.5, 2, 3, 5, 8]
solver = Solver(values=values, dur=3, relError=0.1, monotonous='up')
rater = Rater(relcurve=1.5)
solutions = solve(solver=solver, numslots=4, rater=rater, report=True)
best = solutions[0]
"""
allsolutions = []
possibleNumslots = numslots if misc.isiterable(numslots) else [numslots]
for numslots in possibleNumslots:
solutions = solver.solve(numslots)
allsolutions.extend(solutions)
ratedSolutions = []
for sol in allsolutions:
if rater is not None:
sol = rater(sol)
else:
sol = Solution(sol, 0, None)
ratedSolutions.append(sol)
if rater is not None:
ratedSolutions.sort(reverse=True, key=lambda solution: solution.score)
if report:
reportSolutions(ratedSolutions[:reportMaxRows])
return ratedSolutions
def get_random_prestate_startingwith(self, state):
"""
state can be a single state, a sequence of states or a
string as STATE1:STATE2:etc
It returns None if no state found that starts with the given state
"""
states = _parse_state(state)
assert isiterable(states)
if len(states) > self.order:
raise ValueError(
"The state given has a higher order than this chain")
order = self.order
prestates = [
prestate for prestate in self.get_prestates()
if prestate[:order] == states
]
return random.choice(prestates) if prestates else None
def _parse_state(state: t.U[str, t.Seq]) -> t.List[str]:
if isiterable(state):
return _aslist(state)
return state.split(":")
def plotDurs(durs: List[float],
y0=0.0,
x0=0.0,
height=1.0,
labels: List[str] = None,
color=None,
ax=None,
groupLabel: str = None,
profile: dict = None,
stacked=False) -> plt.Axes:
"""
Plot durations as contiguous rectangles
Args:
durs: the durations expressed in seconds
y0: y of origin
x0: x of origin
height: the height of the drawn rectangles
labels: if given, a label for each rectangle
color: the color used for the rectangles
ax: the axes to draw on. If not given, a new axes is created (and returned)
groupLabel: a label for the group
profile: the profile used, or None to use a default
stacked: if True, the rectangles are drawn stacked vertically (the duration
is still drawn horizontally). The result is then similar to a bars plot
Returns:
the plot axes. If *ax* was given, then it is returned; otherwise the new
axes is returned.
"""
if ax is None:
ax = makeAxis()
numitems = len(durs)
labels = labels if isiterable(labels) else [labels] * numitems
color = _fallbackColor(color, profile, 'facecolor')
if not stacked:
x = x0
data = []
for i, dur in enumerate(durs):
data.append((x, y0, x + dur, y0 + height))
x += dur
drawRects(ax, data, facecolor=color)
if groupLabel is not None:
sep = height * 0.05
y1 = y0 + height
x1 = x0 + sum(durs)
drawBracket(ax,
x0,
y1 + sep,
x1,
y1 + sep * 2,
color=_get(profile, 'annotation_color'))
alpha = (_get(profile, 'annotation_alpha') + 1) * 0.5
drawLabel(ax, (x0 + x1) * 0.5,
y1 + sep,
text=groupLabel,
alpha=alpha)
else:
data = []
y = y0
for dur in durs:
data.append((x0, y, x0 + dur, y + height))
y += height
drawRects(ax, data, facecolor=color)
return ax
def _many(value, numitems: int, key: str = None, profile: dict = None) -> list:
if isiterable(value):
return value
elif value is None:
return [_get(profile, key)] * numitems
return [value] * numitems
