def adjust_for_precedence(self, weights):
prec = {pitch.pc(p):w for p,w in self.precedences.items()}
for k in weights.keys():
if k not in prec:
prec[k] = 1
for p in self.prev_picks:
pcp = pitch.pc(p)
if pcp in prec:
prec[pcp] *= 0.5
mult = {k: prec[pitch.pc(k)] for k in weights.keys()}
mult = w.scale(mult, float(self.precedence) / self.left_to_select)
return w.normalize(w.apply(mult, weights))
def choose(weights):
'''Choose one item out of a weighted list.'''
weights = w.probabilitize(weights)
rand = random.random()
choice = None
for k, v in weights.items():
if rand <= v:
choice = k
break;
rand = rand - v
return choice
### Utility functions
@staticmethod
def identity(x, y): # to be overrided when dealing with pitch classes
return x == y
### Cleaning operations
def clean(self):
self.max_length = len(self.weighted_items) * 2 - 1
self.normalize()
def normalize(self):
self.weighted_items = w.normalize(self.weighted_items)
if __name__ == '__main__':
dm = DataMap({1:0.001, 2:1, 3:5})
weights = dm.weighted_items
print weights
print w.scale(weights, 0.1)