def parseHashPoint(point, numBuckets):
"""Create a LabeledPoint for this observation using hashing.
Args:
point (str): A comma separated string where the first value is the label and the rest are
features.
numBuckets: The number of buckets to hash to.
Returns:
LabeledPoint: A LabeledPoint with a label (0.0 or 1.0) and a SparseVector of hashed
features.
"""
label = point.split(",")[0]
unkeyed_features = point.split(",")[1:]
index = 0
keyed_features = []
for feature in unkeyed_features:
keyed_features.append((index, feature))
index += 1
features = hashFunction(numBuckets, keyed_features, True)
features = SparseVector(numBuckets, sorted(features.keys()), features.values())
return LabeledPoint(label, features)
def f(champ): i = 0 newVects = [] while champ + i * (max(champions) + 1) < len(partialVect): newVect = SparseVector(len(partialVect), partialVect.indices, partialVect.values) newVect.indices = numpy.append(newVect.indices, [champ + i * (max(champions) + 1)]) newVect.values = numpy.append(newVect.values, [sign]) newVects.append(newVect) i += 1 return newVects
