def train(self, features, labels, normalisedlabels=False, names=None, **kwargs):
N,M = features.shape
m = int(self.frac*M)
n = int(self.frac*M)
R = np.random
tree = milk.supervised.tree.tree_learner()
forest = []
if not normalisedlabels:
labels,names = normaliselabels(labels)
elif names is None:
names = (0,1)
for i in xrange(self.rf):
forest.append(
tree.train(*_sample(features, labels, n, R),
**{'normalisedlabels' : True})) # This syntax is necessary for Python 2.5
return rf_model(forest, names)
def train(self, features, labels, normalisedlabels=False, names=None, return_label=True, **kwargs):
N,M = features.shape #N is number of training samples, M is the number of features.
m = int(self.frac*M) #Building a tree with a fraction of the features
n = int(self.frac*N) #Building a tree on a fraction of the training set
R = get_nprandom(kwargs.get('R', self.R))
tree = milk.supervised.tree.tree_learner(return_label=return_label)
forest = []
if not normalisedlabels:
labels,names = normaliselabels(labels)
elif names is None:
names = (0,1) #Ok. Not quite sure when we'll reach here.
for i in xrange(self.rf):
forest.append(
tree.train(*_sample(features, labels, n, R),
**{'normalisedlabels' : True})) # This syntax is necessary for Python 2.5
#This is btw, just a complicated way of passing (feature, label, normalizedLabel=True)
return rf_model(forest, names, return_label)
def train(self,
features,
labels,
normalisedlabels=False,
names=None,
**kwargs):
N, M = features.shape
m = int(self.frac * M)
n = int(self.frac * M)
R = np.random
tree = milk.supervised.tree.tree_learner()
forest = []
if not normalisedlabels:
labels, names = normaliselabels(labels)
elif names is None:
names = (0, 1)
for i in xrange(self.rf):
forest.append(
tree.train(*_sample(features, labels, n, R),
**{'normalisedlabels': True
})) # This syntax is necessary for Python 2.5
return rf_model(forest, names)
