Python option 예제들

예제 #1

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
import script
import ontology
import dataset
import graph
import collections
import ml
import numpy as np

TEST_SAMPLES = int(script.option('TEST_SAMPLES'))
OUTPUT       = script.option('OUTPUT')

features = ontology.features
rooms = ontology.rooms

world = dataset.DiscreteDistribution([(1, dist) for cat,dist in rooms.items()])
test_samples = dataset.LabelledSample(world, TEST_SAMPLES)
#############################################################################

def map_classifier(sample):
  best = max([(dataset.SampleProbability(dist, sample), label) for label, dist in rooms.items()])
  return best[1]


confusion = collections.defaultdict(int)

예제 #2

파일: ontology.py 프로젝트: andresusanopinto/novelty-detection-thesis

#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# This module loads the ontology and creates distributions defined by:
# default_knowledge-semmap.xml and defaultprobs-semmap.txt
#
import script
import re
import math
import dataset
from collections import defaultdict

POISSON_EXPANSION = 3
DEFAULT_KNOWLEDGE = script.option('ONTOLOGY_DEFAULT_KNOWLEDGE')
DEFAULT_OBJ_PROBS = script.option('ONTOLOGY_DEFAULT_OBJ_PROBS')
DROP_FEATURES     = script.option('ONTOLOGY_DROP_FEATURES').split()


potential = dict()
roomcats  = set()
features  = set()
feature_space = defaultdict(set)

def Load():
  print("Loading default knowledge...")
  for line in open(DEFAULT_KNOWLEDGE):
    match = re.match('^\s+<item\s+room_category1="(?P<roomcat>\w+)"\s+' + 
                     '(?P<proptype>\w+)="(?P<propval>\w+)"\s+' + 
                     '(?:potential|probability)="(?P<potential>\S+)"\s*\/>\s*$', line)

예제 #3

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
import script
import ontology
import random
import dataset
import graph
import collections
import ml

KNOWN_LABELS = script.option('KNOWN_LABELS').split()
LABELLED_SAMPLES = int(script.option('LABELLED_SAMPLES'))
UNLABELLED_SAMPLES = int(script.option('UNLABELLED_SAMPLES'))
TEST_SAMPLES = int(script.option('TEST_SAMPLES'))
PLOT = script.option('PLOT').split()
OUTPUT = script.option('OUTPUT')

features = ontology.features
rooms = ontology.rooms

#############################################################################

all_labels = rooms.keys()
world = dataset.DiscreteDistribution([(1, dist)
                                      for cat, dist in rooms.items()])
known = dataset.DiscreteDistribution([(1, dist) for cat, dist in rooms.items()

예제 #4

파일: run.py 프로젝트: andresusanopinto/novelty-detection-thesis

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
import script
import ontology
import random
import dataset
import graph
import collections
import ml

KNOWN_LABELS       = script.option('KNOWN_LABELS').split()
LABELLED_SAMPLES   = int(script.option('LABELLED_SAMPLES'))
UNLABELLED_SAMPLES = int(script.option('UNLABELLED_SAMPLES'))
TEST_SAMPLES       = int(script.option('TEST_SAMPLES'))
PLOT         = script.option('PLOT').split()
OUTPUT       = script.option('OUTPUT')


features = ontology.features
rooms = ontology.rooms

#############################################################################



all_labels = rooms.keys()

예제 #5

#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# This module loads the ontology and creates distributions defined by:
# default_knowledge-semmap.xml and defaultprobs-semmap.txt
#
import script
import re
import math
import dataset
from collections import defaultdict

POISSON_EXPANSION = 3
DEFAULT_KNOWLEDGE = script.option('ONTOLOGY_DEFAULT_KNOWLEDGE')
DEFAULT_OBJ_PROBS = script.option('ONTOLOGY_DEFAULT_OBJ_PROBS')
DROP_FEATURES = script.option('ONTOLOGY_DROP_FEATURES').split()

potential = dict()
roomcats = set()
features = set()
feature_space = defaultdict(set)


def Load():
    print("Loading default knowledge...")
    for line in open(DEFAULT_KNOWLEDGE):
        match = re.match(
            '^\s+<item\s+room_category1="(?P<roomcat>\w+)"\s+' +
            '(?P<proptype>\w+)="(?P<propval>\w+)"\s+' +

예제 #6

import script
import explain
from ontology import features, rooms

out = explain.ExplainDistributions(sorted(features.items()),
                                   sorted(rooms.items()))

script.WriteFile(script.option('OUTPUT'), out)