本文整理匯總了Python中markov.Markov.predict方法的典型用法代碼示例。如果您正苦於以下問題：Python Markov.predict方法的具體用法？Python Markov.predict怎麽用？Python Markov.predict使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類markov.Markov的用法示例。
在下文中一共展示了Markov.predict方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: featureList
# 需要導入模塊: from markov import Markov [as 別名]
# 或者: from markov.Markov import predict [as 別名]
def featureList(pairs):
features = list()
markov = Markov()
# go from the feature representation of the data (features) to the original data (pairs)
dataFeatToData = {}
for bar in pairs:
seq = pairs[bar]
isoFeatures = {}
isoFeaturesOrig = {}
rootScores = {}
# each data element should have as a features all the features of the previous and subsequent tslices
for i in range(len(seq)):
data,label = seq[i]
chordKey = data[8]
# isolated features
isoFeatures[i] = (getIsoFeatures(data),label)
isoFeaturesOrig[i] = isoFeatures[i]
rootScores[i] = [int(dat) for dat in data[:7]]
for i in range(len(seq)):
# subsequent measure
subBar = bar.split('_')[0] + str(int(bar.split('_')[1])+1)
if i < len(seq) - 1:
subFeats = dict([('S_' + key, val) for (key,val) in isoFeaturesOrig[i+1][0].items()])
isoFeatures[i] = (dict(isoFeatures[i][0].items() + subFeats.items()) , isoFeatures[i][1])
elif subBar in pairs:
subFeats = dict([('S_' + key, val) for (kev,val) in getIsoFeatures(pairs[subBar][0][0]).items()])
isoFeatures[i] = (dict(isoFeatures[i][0].items() + subFeats.items()) , isoFeatures[i][1])
# prev measure
prevBar = bar.split('_')[0] + str(int(bar.split('_')[1])-1)
if i > 0:
prevFeats = dict([('P_' + key, val) for (key,val) in isoFeaturesOrig[i-1][0].items()])
#markovPred = markov.predict(chordKey, tuple([rootScores[i-1]]), markov.markov2)
markovFeats = [('markov2', markov.predict(chordKey, tuple([rootScores[i-1]]), markov.markov2))]
if i > 1:
markovFeats += [('markov3', markov.predict(chordKey, tuple([rootScores[i-2], rootScores[i-1]]), markov.markov3))]
if i > 2:
markovFeats += [('markov4', markov.predict(chordKey, tuple([rootScores[i-3], rootScores[i-2], rootScores[i-1]]), markov.markov4))]
isoFeatures[i] = (dict(isoFeatures[i][0].items() + prevFeats.items() + markovFeats) , isoFeatures[i][1])
#isoFeatures[i] = (dict(isoFeatures[i][0].items() + prevFeats.items()) , isoFeatures[i][1])
elif prevBar in pairs:
prevFeats = dict([('P_' + key, val) for (kev,val) in getIsoFeatures(pairs[prevBar][-1][0]).items()])
isoFeatures[i] = (dict(isoFeatures[i][0].items() + subFeats.items()) , isoFeatures[i][1])
dataFeatToData[str(isoFeatures[i])] = seq[i]
features += isoFeatures.values()
return dataFeatToData, features