def __init__(self):
"""
Configuration
"""
# Camera settings
self.FRAME_WIDTH = 341
self.FRAME_HEIGHT = 256
self.flip_camera = True # Mirror image
self.camera = cv2.VideoCapture(1)
# ...you can also use a test video for input
#video = "/Users/matthiasendler/Code/snippets/python/tracker/final/assets/test_video/10.mov"
#self.camera = cv2.VideoCapture(video)
#self.skip_input(400) # Skip to an interesting part of the video
if not self.camera.isOpened():
print "couldn't load webcam"
return
#self.camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, self.FRAME_WIDTH)
#self.camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, self.FRAME_HEIGHT)
self.filters_dir = "filters/" # Filter settings in trackbar
self.filters_file = "filters_default"
# Load filter settings
current_config = self.filters_dir + self.filters_file
self.filters = Filters(current_config)
# No actions will be triggered in test mode
# (can be used to adjust settings at runtime)
self.test_mode = False
# Create a hand detector
# In fact, this is a wrapper for many detectors
# to increase detection confidence
self.detector = Detector(self.filters.config)
# Knowledge base for all detectors
self.kb = KB()
# Create gesture recognizer.
# A gesture consists of a motion and a hand state.
self.gesture = Gesture()
# The action module executes keyboard and mouse commands
self.action = Action()
# Show output of detectors
self.output = Output()
self.run()
class Tracker(object):
"""
This is the main program which gives a high-level view
of all the running subsystems. It connects camera input with
output in form of "actions" (such as keyboard shortcuts on the users behalf).
This is done by locating a hand in an image and detecting features,
like the number of fingers, and trying to match that data with a
known gesture.
"""
def __init__(self):
"""
Configuration
"""
# Camera settings
self.FRAME_WIDTH = 341
self.FRAME_HEIGHT = 256
self.flip_camera = True # Mirror image
self.camera = cv2.VideoCapture(1)
# ...you can also use a test video for input
#video = "/Users/matthiasendler/Code/snippets/python/tracker/final/assets/test_video/10.mov"
#self.camera = cv2.VideoCapture(video)
#self.skip_input(400) # Skip to an interesting part of the video
if not self.camera.isOpened():
print "couldn't load webcam"
return
#self.camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, self.FRAME_WIDTH)
#self.camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, self.FRAME_HEIGHT)
self.filters_dir = "filters/" # Filter settings in trackbar
self.filters_file = "filters_default"
# Load filter settings
current_config = self.filters_dir + self.filters_file
self.filters = Filters(current_config)
# No actions will be triggered in test mode
# (can be used to adjust settings at runtime)
self.test_mode = False
# Create a hand detector
# In fact, this is a wrapper for many detectors
# to increase detection confidence
self.detector = Detector(self.filters.config)
# Knowledge base for all detectors
self.kb = KB()
# Create gesture recognizer.
# A gesture consists of a motion and a hand state.
self.gesture = Gesture()
# The action module executes keyboard and mouse commands
self.action = Action()
# Show output of detectors
self.output = Output()
self.run()
def run(self):
"""
In each step: Read the input image and keys,
process it and react on it (e.g. with an action).
"""
while True:
img = self.get_input()
hand = self.process(img)
ref = self.action.get_reference_point()
self.output.show(img, hand, ref)
def process(self, img):
"""
Process input
"""
# Run detection
hand = self.detector.detect(img)
# Store result in knowledge base
self.kb.update(hand)
if not self.test_mode:
# Try to interprete as gesture
self.interprete(hand)
return hand
def interprete(self, hand):
"""
Try to interprete the input as a gesture
"""
self.gesture.add_hand(hand)
operation = self.gesture.detect_gesture()
self.action.execute(operation)
def get_input(self):
"""
Get input from camera and keyboard
"""
self.get_key()
_, img = self.camera.read()
img = cv2.resize(img, (self.FRAME_WIDTH, self.FRAME_HEIGHT))
if self.flip_camera:
img = cv2.flip(img, 1)
return img
def get_key(self):
"""
Read keyboard input
"""
key = cv2.waitKey(self.filters.config["wait_between_frames"])
if key == ord('+'):
# Reduce program speed
self.filters.config["wait_between_frames"] += 500
if key == ord('-'):
# Increase program speed
if self.filters.config["wait_between_frames"] >= 500:
self.filters.config["wait_between_frames"] -= 500
#if key == ord('s'):
# Save config
# self.filters.save()
if key == ord('r'):
# Reset all detectors
self.detector.reset()
self.action.reset()
if key == ord('d'):
# Make a screenshot
self.output.make_screenshot()
if key == ord('p') or key == ord(' '):
# Pause
cv2.waitKey()
if key == ord('t'):
# Test mode
self.test_mode = not self.test_mode
if key == ord('1'):
self.output.toggle_estimate()
if key == ord('2'):
self.output.toggle_detectors()
if key == ord('3'):
self.output.toggle_skin()
if key == ord('f'):
self.toggle_filters()
if key == 63235: # Right arrow
self.skip_input(20)
if key == 27 or key == ord('q'):
# Abort program on ESC, q or space
exit()
def toggle_filters(self):
"""
Load the next filter settings
"""
self.filters_file = self.next_filters_file()
current_config = self.filters_dir + self.filters_file
self.filters.set_config(current_config)
def next_filters_file(self):
"""
Get the next filter settings
"""
filters = listdir(self.filters_dir)
for i, f in enumerate(filters):
if f == self.filters_file:
return filters[(i+1) % len(filters)]
def skip_input(self, x=1):
"""
Skip to a different part of a video sequence.
"""
for i in range(0,x):
self.camera.grab()
hence we play the lowest possible card. If the played card is not from the trump suit, we first check for a card
stronger than that card, then we look for a trump card to win that trick. If these conditions are not fulfilled,
it can be predicted that we loose that trick, hence we simply play the lowest non-trump card. Again, if we don't have
such a card, we play the lowest possible card.
It loads general information about the game, as well as the definition of a strategy,
from load.py.
"""
from api import State, util, Deck
import random, load, copy
from bots.ultra import kb
from kb import KB, Boolean, Integer
kb = KB()
THRESHOLD = 10
class Bot:
__RANKS = ["a", "t", "k", "q", "j"]
def __init__(self):
pass
def get_move(self, state):
whose_turn = state.whose_turn()
leader = state.leader()
trump_suit = state.get_trump_suit()
if whose_turn == leader:
global kb
def __init__(self, num_relations, num_entities,
arities=[0.0, 1.0, 0.0],
fb_densities=[0.0, 0.0, 0.0],
arg_densities=[0., 0.1, 0.0],
fact_prob=0.2,
num_symm=2,
num_impl=[0, 2, 0],
num_impl_inv=2,
num_impl_conj=[0, 2, 0],
num_trans_single=2,
num_trans_diff=2,
seed=0,
position_dependent_args=False,
position_densities=[0., 0.5, 0.0]):
"""
:param num_relations:
:param num_entities: number of distinct entities to generate
:param arities: fraction of arities
:param arg_densities: fraction of entity combinations that are observed
:param fact_prob:
:param num_inv: number of 'inv' formulae R(X0, X1) :- R(X1, X0)
:param num_impl:
:param num_impl_conj:
:param num_trans:
:param negated_head_prob:
:param seed:
:return:
"""
random.seed(seed)
self.kb = KB(seed=seed)
num_relations_per_arity = [int(x * num_relations) for x in arities]
entities = list(map(lambda x: "e" + str(x), range(1, num_entities+1)))
entities_arg1 = []
entities_arg2 = []
entities_arg3 = []
if position_dependent_args:
arg1_boundary = int(len(entities)*position_densities[0])
arg2_boundary = arg1_boundary + int(len(entities)*position_densities[1])
entities_arg1 = entities[0:arg1_boundary]
entities_arg2 = entities[arg1_boundary:arg2_boundary]
entities_arg3 = entities[arg2_boundary:]
else:
entities_arg1 = entities
entities_arg2 = entities
entities_arg3 = entities
pairs = [(x, y) for x in entities_arg1
for y in entities_arg2 if not x == y]
triples = [(x, y, z) for x in entities_arg1
for y in entities_arg2 for z in entities_arg3
if not x == y and not y == z and not z == x]
num_pair_samples = min(len(pairs), int(len(entities_arg1) *
len(entities_arg2) *
arg_densities[1]))
num_triple_samples = min(len(triples), int(len(entities_arg1) *
len(entities_arg2) *
len(entities_arg3) *
arg_densities[2]))
entities_per_arity = {
1: entities_arg1,
2: random.sample(pairs, num_pair_samples),
3: random.sample(triples, num_triple_samples)
}
relations_per_arity = {}
for arity in range(1, len(num_relations_per_arity) + 1):
for i in range(1, num_relations_per_arity[arity - 1] + 1):
fb_prefix = ""
if fb_densities[arity-1] > random.uniform(0, 1.0):
fb_prefix = "REL$"
if arity == 1:
rel = fb_prefix+"u"
elif arity == 2:
rel = fb_prefix+"b"
else:
rel = fb_prefix+"t"
rel += str(i)
if not arity in relations_per_arity:
relations_per_arity[arity] = list()
relations_per_arity[arity].append(rel)
for args in random.sample(entities_per_arity[arity],
int(len(entities_per_arity[arity]) * fact_prob)):
self.kb.add_train(rel, args)
inverse = []
# sample symmetric relations r(X,Y) => r(Y,X)
if 2 in relations_per_arity:
symm = random.sample([(x, x) for x in relations_per_arity[2]], num_symm)
inverse += symm
# sampling implication, reversed: r1(X,Y) => r2(Y,X)
if 2 in relations_per_arity:
inverse += random.sample([(x, y) for x in relations_per_arity[2]
for y in relations_per_arity[2]
if not x == y], num_impl_inv)
if len(inverse) > 0:
self.kb.add_formulae("inv", {2: inverse})
# sampling implications:
# r1(X) => r2(X)
# r1(X,Y) => r2(X,Y)
implications_per_arity = {}
for arity in range(1, len(num_relations_per_arity) + 1):
if arity in relations_per_arity:
implications_per_arity[arity] = \
random.sample([(x, y) for x in relations_per_arity[arity] for y in relations_per_arity[arity]
if not x == y], num_impl[arity - 1])
self.kb.add_formulae("impl", implications_per_arity)
# sampling implications with conjunction in body:
# r1(X,Y) ^ r2(X,Y) => r3(X,Y)
# r1(X) ^ r2(X) => r3(X)
implications_with_conjunction_per_arity = {}
for arity in range(1, len(num_relations_per_arity) + 1):
if arity in relations_per_arity and len(relations_per_arity[arity]) >= 3:
implications_with_conjunction_per_arity[arity] = \
random.sample([(x, y, z) for x in relations_per_arity[arity]
for y in relations_per_arity[arity]
for z in relations_per_arity[arity]
if not x == y and not y == z and not z == x],
num_impl_conj[arity - 1])
self.kb.add_formulae("impl_conj", implications_with_conjunction_per_arity)
# sampling transitivities:
transitivities = []
# (1) simple transitivities r(X,Y) ^ r(Y,Z) => r(X,Z)
# (2) general transitivities r1(X,Y) ^ r2(Y,Z) => r3(X,Z) (r1, r2, r3 differ)
if 2 in relations_per_arity:
if num_trans_single > 0:
transitivities += random.sample([(x, x, x)
for x in relations_per_arity[2]], num_trans_single)
if num_trans_diff > 0:
transitivities += random.sample([(x, y, z)
for x in relations_per_arity[2]
for y in relations_per_arity[2]
for z in relations_per_arity[2]
if not x == y and
not y == z and
not z == x], num_trans_diff)
if len(transitivities) > 0:
self.kb.add_formulae("trans", {2: transitivities})
def init(self,
kb_host="localhost",
kb_port=6969,
embeddedkb=False,
defaultontology=None,
data_path=None):
if not data_path:
# try to guess the current prefix and then the data directory
data_path = os.path.abspath(__file__).split('lib')[0].split(
'src')[0] + 'share/dialogs/'
logger.debug("Assuming Dialogs data dir is <%s>" % data_path)
try:
from kb import KB, KbError
except ImportError:
logger.error("Python bindings to access the knowledge are not available." + \
"Please install 'pykb' and restart Dialogs.")
try:
self.ontology_server = KB(kb_host, kb_port, embeddedkb,
defaultontology)
except KbError:
logger.error("Error while trying to connect to the knowledge base on %s:%s" % (kb_host, kb_port) + \
". Continuing without knowledge base. Amongst others, resolution won't work.")
self.ontology_server = DummyKnowledgeBase()
for line in open(os.path.join(data_path, "adjectives")):
if line.startswith("#") or not line.strip():
continue
try:
adj, cat = line.split()
except ValueError: # for adjectives without category, set a generic "Feature" category
adj = line.split()[0]
cat = "Feature"
self.adjectives[adj] = cat
verbs = [
list(line.split())
for line in open(os.path.join(data_path, "verbs"))
]
verbs = self.split_list(verbs)
self.irregular_verbs_past = verbs[0]
self.irregular_verbs_present = verbs[1]
self.preposition_verbs = verbs[2]
self.modal = [k[0] for k in verbs[3]]
self.adjective_verb = [k[0] for k in verbs[4]]
self.auxiliary = [k[0] for k in verbs[5]]
self.direct_transitive = [k[0] for k in verbs[6]]
self.indirect_transitive = [k[0] for k in verbs[7]]
self.state = [k[0] for k in verbs[8]]
self.verb_need_to = [k[0] for k in verbs[9]]
self.special_verbs = [k[0] for k in verbs[12]]
# Action verbs such as 'see', 'hear' with no active behaviour
self.action_verb_with_passive_behaviour = dict([(k[0], k[1])
for k in verbs[10]])
self.goal_verbs = [k[0] for k in verbs[11]]
self.sentence_starts = [
tuple(line.split())
for line in open(os.path.join(data_path, "sentence_starts"))
]
nouns = [
list(line.split())
for line in open(os.path.join(data_path, "nouns"))
]
nouns = self.split_list(nouns)
self.special_nouns = [k[0] for k in nouns[0]]
self.pronouns = [k[0] for k in nouns[1]]
for i in nouns[1]:
if i[1] == '1':
self.complement_pronouns = self.complement_pronouns + [i[0]]
self.demonstrative_det = [k[0] for k in nouns[2]]
self.determinants = [k[0] for k in nouns[3]]
self.nouns_end_s = [k[0] for k in nouns[4]]
self.relatives = [k[0] for k in nouns[5]]
self.composed_nouns = [k[0] for k in nouns[6]]
self.plural_nouns = nouns[7]
self.noun_not_composed = [k[0] for k in nouns[8]]
self.days_list = nouns[9]
self.months_list = nouns[10]
self.unusable_words = [k[0] for k in nouns[11]]
# List of diection words, E.g: LEFT, RIGHT, TOP, etc ...
self.direction_words = [k[0] for k in nouns[12]]
self.compound_nouns = nouns[13]
###
### ADVERBIALS
adverbials = [
list(line.split())
for line in open(os.path.join(data_path, "adverbial"))
]
adverbials = self.split_list(adverbials)
self.adverbs = [k[0] for k in adverbials[0]]
self.time_adverbs = adverbials[0]
self.time_adverbs = [
k[0] for k in adverbials[0] if k[1] in ["day", "hour"]
]
self.time_adverbs += [
k[0] for k in adverbials[1] if k[1] in ["day", "hour"]
]
self.location_adverbs = [
k[0] for k in adverbials[0] if k[1] == "location"
]
self.location_adverbs += [
k[0] for k in adverbials[1] if k[1] == "location"
]
self.adverbs_at_end = [k[0] for k in adverbials[1]]
for k in adverbials[2]:
if k[1] == '1':
self.compelement_proposals = self.compelement_proposals + [
k[0]
]
self.proposals = [k[0] for k in adverbials[2]]
#Preposition with an existing object_property
# E.g: next+to => isNextTo
self.preposition_rdf_object_property = dict([(k[0], k[3:])
for k in adverbials[2]])
self.time_proposals = adverbials[2]
self.subsentences = [k[0] for k in adverbials[3]]
for k in adverbials[3]:
if k[1] == '1':
self.adv_sub = self.adv_sub + [k[0]]
self.prep_change_place = [k[0] for k in adverbials[4]]
grammatical_rules = [
list(line.split())
for line in open(os.path.join(data_path, "grammatical_rules"))
]
grammatical_rules = self.split_list(grammatical_rules)
self.numbers = grammatical_rules[0]
self.det_quantifiers = grammatical_rules[1]
self.capital_letters = [k[0] for k in grammatical_rules[2]]
self.adjective_rules = [k[0] for k in grammatical_rules[3]]
self.concatenate_proposals = grammatical_rules[4]
self.change_tuples = grammatical_rules[5]
self.adjective_numbers_digit = grammatical_rules[6]
self.adjective_numbers = [k[0] for k in grammatical_rules[6]]
self.be_pronoun = [k[0] for k in grammatical_rules[7]]
self.adj_quantifiers = [k[0] for k in grammatical_rules[8]]
for k in grammatical_rules[9]:
self.replace_tuples = self.replace_tuples + [[k[0], k[1:]]]
desc = ""
for line in open(os.path.join(data_path, "thematic_roles")):
if line.startswith("#") or not line.strip():
continue
desc += line
if line.startswith("}"): #end of block
self.thematic_roles.add_verb(desc)
desc = ""
#Add action verbs to the ontology
if self.ontology_server:
stmts = [
verb.capitalize() + " rdfs:subClassOf cyc:PurposefulAction"
for verb in self.thematic_roles.verbs.keys()
if not self.thematic_roles.verbs[verb].is_synonym()
]
self.ontology_server.revise(stmts, {"method": "add"})
"""
List of ontology classes that are used in the adjectives list
"""
self.adjectives_ontology_classes = [
self.adjectives[adj].lower() for adj in self.adjectives
]
adj_s = []
for k in self.adjectives_ontology_classes:
if not k in adj_s:
adj_s.append(k)
self.adjectives_ontology_classes = adj_s
PA7 = Boolean('pa7')
PA8 = Boolean('pa8')
PA9 = Boolean('pa9')
PA10 = Boolean('pa10')
PA11 = Boolean('pa11')
PA12 = Boolean('pa12')
PA13 = Boolean('pa13')
PA14 = Boolean('pa14')
PA15 = Boolean('pa15')
PA16 = Boolean('pa16')
PA17 = Boolean('pa17')
PA18 = Boolean('pa18')
PA19 = Boolean('pa19')
# Create a new knowledge base
kb = KB()
# GENERAL INFORMATION ABOUT THE CARDS
# This adds information which cards are Jacks
kb.add_clause(J4)
kb.add_clause(J9)
kb.add_clause(J14)
kb.add_clause(J19)
# Add here whatever is needed for your strategy.
kb.add_clause(A0)
kb.add_clause(A5)
kb.add_clause(A10)
kb.add_clause(A15)
# DEFINITION OF THE STRATEGY
# Add clauses (This list is sufficient for this strategy)
# PJ is the strategy to play jacks first, so all we need to model is all x PJ(x) <-> J(x),
import sys
from kb import KB, Boolean, Integer, Constant
# Define our symbols
P = Boolean('P')
Q = Boolean('Q')
R = Boolean('R')
# Create a new knowledge base
kb = KB()
# Add clauses
kb.add_clause(P, Q)
kb.add_clause(~Q, R)
kb.add_clause(~R, ~P)
kb.add_clause(~P, ~Q)
# Print all models of the knowledge base
for model in kb.models():
print(model)
# Print out whether the KB is satisfiable (if there are no models, it is not satisfiable)
print(kb.satisfiable())
import sys
from kb import KB, Boolean, Integer, Constant
# Define our integer symbols
x = Integer('x')
y = Integer('y')
q = x == y
a = x + y > 2
b = x + y < 5
c = x + y < -2
d = x + y > -5
kb = KB()
kb.add_clause(q)
kb.add_clause(???)
kb.add_clause(???)
kb.add_clause(???)
kb.add_clause(???)
for model in kb.models():
print(model)
from kb import KB
from fact import Fact
import sys
kb = KB()
kb_file = sys.argv[1]
query_file = sys.argv[2]
answer_file = sys.argv[3]
kb_file = open(kb_file, 'r')
clauses = kb_file.readlines()
KB.CreateKB(kb, clauses)
query_file = open(query_file, 'r')
answer_file = open(answer_file, 'w')
count_query = 1
for query in query_file.readlines():
alpha = Fact.ParseFact(query)
alpha_str = str(alpha) + '.'
print(str(count_query) + "/", alpha_str)
substs = set(kb.Query(alpha))
substs_str = ' ;\n'.join([str(subst) for subst in substs]) + '.\n'
print(substs_str)
answer_file.write(alpha_str + '\n')
answer_file.write(substs_str + '\n')
count_query += 1
def kb_consistent(self, state, move):
# type: (State, move) -> bool
# each time we check for consistency we initialise a new knowledge-base
kb = KB()
# Add general information about the game
load.general_information(kb)
# Add the necessary knowledge about the strategy
load.strategy_knowledge(kb)
# This line stores the index of the card in the deck.
# If this doesn't make sense, refer to _deck.py for the card index mapping
index = move[0]
# This creates the string which is used to make the strategy_variable.
# Note that as far as kb.py is concerned, two objects created with the same
# string in the constructor are equivalent, and are seen as the same symbol.
# Here we use "pj" to indicate that the card with index "index" should be played with the
# PlayJack heuristics that was defined in class. Initialise a different variable if
# you want to apply a different strategy (that you will have to define in load.py)
# # Always play trump
trump_suit = state.get_trump_suit()
trump = trump_suit.lower()
if trump == 'c':
pj = Boolean('pj4')
pq = Boolean('pq3')
pk = Boolean('pk2')
pt = Boolean('pt1')
pa = Boolean('pa0')
if trump == 'd':
pj = Boolean('pj9')
pq = Boolean('pq8')
pk = Boolean('pk7')
pt = Boolean('pt6')
pa = Boolean('pa5')
if trump == 'h':
pj = Boolean('pj14')
pq = Boolean('pq13')
pk = Boolean('pk12')
pt = Boolean('pt11')
pa = Boolean('pa10')
if trump == 's':
pj = Boolean('pj19')
pq = Boolean('pq18')
pk = Boolean('pk17')
pt = Boolean('pt16')
pa = Boolean('pa15')
kb.add_clause(~pj, ~pq, ~pk, ~pt, ~pa)
# # always play Jack
# variable_string = "pj" + str(index)
# strategy_variable = Boolean(variable_string)
# Add the relevant clause to the loaded knowledge base
# kb.add_clause(~strategy_variable)
# If the knowledge base is not satisfiable, the strategy variable is
# entailed (proof by refutation)
return kb.satisfiable()
PJ7 = Boolean('pj7')
PJ8 = Boolean('pj8')
PJ9 = Boolean('pj9')
PJ10 = Boolean('pj10')
PJ11 = Boolean('pj11')
PJ12 = Boolean('pj12')
PJ13 = Boolean('pj13')
PJ14 = Boolean('pj14')
PJ15 = Boolean('pj15')
PJ16 = Boolean('pj16')
PJ17 = Boolean('pj17')
PJ18 = Boolean('pj18')
PJ19 = Boolean('pj19')
# Create a new knowledge base
kb = KB()
# GENERAL INFORMATION ABOUT THE CARDS
# This adds information which cards are Jacks
kb.add_clause(J4)
kb.add_clause(J9)
kb.add_clause(J14)
kb.add_clause(J19)
# Adds information on which cards are As
kb.add_clause(J0)
kb.add_clause(J5)
kb.add_clause(J10)
kb.add_clause(J15)
# DEFINITION OF THE STRATEGY
# Add clauses (This list is sufficient for this strategy)
def main(args):
if (not args.do_train) and (not args.do_valid) and (not args.do_test):
raise ValueError('one of train/val/test mode must be choosed.')
if args.init_checkpoint:
override_config(args)
elif args.data_path is None:
raise ValueError('one of init_checkpoint/data_path must be choosed.')
if args.do_train and args.save_path is None:
raise ValueError('Where do you want to save your trained model?')
if args.save_path and not os.path.exists(args.save_path):
os.makedirs(args.save_path)
# Write logs to checkpoint and console
set_logger(args)
with open(os.path.join(args.data_path, 'entities.dict')) as fin:
entity2id = dict()
id2entity = dict()
for line in fin:
eid, entity = line.strip().split('\t')
entity2id[entity] = int(eid)
id2entity[int(eid)] = entity
with open(os.path.join(args.data_path, 'relations.dict')) as fin:
relation2id = dict()
id2relationship = dict()
for line in fin:
rid, relation = line.strip().split('\t')
relation2id[relation] = int(rid)
id2relationship[int(rid)] = relation
# Read regions for Countries S* datasets
if args.countries:
regions = list()
with open(os.path.join(args.data_path, 'regions.list')) as fin:
for line in fin:
region = line.strip()
regions.append(entity2id[region])
args.regions = regions
e_vocab = Dictionary(tok2ind=entity2id, ind2tok=id2entity)
r_vocab = Dictionary(tok2ind=relation2id, ind2tok=id2relationship)
## TODO: add graph file
graph = KB(os.path.join(args.data_path, 'train.txt'),
e_vocab=e_vocab,
r_vocab=r_vocab)
nentity = len(entity2id)
nrelation = len(relation2id)
args.nentity = nentity
args.nrelation = nrelation
logging.info('Model: %s' % args.model)
logging.info('Data Path: %s' % args.data_path)
logging.info('#entity: %d' % nentity)
logging.info('#relation: %d' % nrelation)
train_triples = read_triple(os.path.join(args.data_path, 'train.txt'),
entity2id, relation2id)
logging.info('#train: %d' % len(train_triples))
valid_triples = read_triple(os.path.join(args.data_path, 'valid.txt'),
entity2id, relation2id)
logging.info('#valid: %d' % len(valid_triples))
test_triples = read_triple(os.path.join(args.data_path, 'test.txt'),
entity2id, relation2id)
logging.info('#test: %d' % len(test_triples))
candidate_entities = None
if args.rerank_minerva:
candidate_entities = defaultdict(set)
with open(
"/home/shdhulia//Limits-of-Path-Reasoner/outputs/FB15K-237/thisone_test/all_answers.txt"
) as candidate_file:
# with open("/home/shdhulia/minerva_answers/fb.txt") as candidate_file:
for line in candidate_file:
pt = line.strip().split("\t")
e1 = entity2id[pt[0]]
r = relation2id[pt[1]]
predicted_es = set(
[entity2id[p] for p in pt[2:] if p in entity2id])
candidate_entities[(e1, r)] = set(predicted_es)
#All true triples
all_true_triples = train_triples + valid_triples + test_triples
kge_model = KGEModel(
model_name=args.model,
nentity=nentity,
nrelation=nrelation,
hidden_dim=args.hidden_dim,
gamma=args.gamma,
double_entity_embedding=args.double_entity_embedding,
double_relation_embedding=args.double_relation_embedding)
logging.info('Model Parameter Configuration:')
for name, param in kge_model.named_parameters():
logging.info('Parameter %s: %s, require_grad = %s' %
(name, str(param.size()), str(param.requires_grad)))
if args.cuda:
kge_model = kge_model.cuda()
if args.do_train:
# Set training dataloader iterator
# train_dataloader_head = DataLoader(
# TrainDataset(train_triples, nentity, nrelation, args.negative_sample_size, 'head-batch'),
# batch_size=args.batch_size,
# shuffle=True,
# num_workers=max(1, args.cpu_num//2),
# collate_fn=TrainDataset.collate_fn
# )
train_dataloader_tail = DataLoader(
TrainDataset(train_triples,
nentity,
nrelation,
args.negative_sample_size,
'tail-batch',
KB=graph),
batch_size=args.batch_size,
shuffle=True,
num_workers=max(1, args.cpu_num // 2),
collate_fn=TrainDataset.collate_fn)
# train_iterator = BidirectionalOneShotIterator(train_dataloader_head, train_dataloader_tail)
train_iterator = OneShotIterator(train_dataloader_tail)
# Set training configuration
current_learning_rate = args.learning_rate
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
kge_model.parameters()),
lr=current_learning_rate)
if args.warm_up_steps:
warm_up_steps = args.warm_up_steps
else:
warm_up_steps = args.max_steps // 2
if args.init_checkpoint:
# Restore model from checkpoint directory
logging.info('Loading checkpoint %s...' % args.init_checkpoint)
checkpoint = torch.load(
os.path.join(args.init_checkpoint, 'checkpoint'))
init_step = checkpoint['step']
kge_model.load_state_dict(checkpoint['model_state_dict'])
if args.do_train:
current_learning_rate = checkpoint['current_learning_rate']
warm_up_steps = checkpoint['warm_up_steps']
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
else:
logging.info('Ramdomly Initializing %s Model...' % args.model)
init_step = 0
step = init_step
logging.info('Start Training...')
logging.info('init_step = %d' % init_step)
logging.info('batch_size = %d' % args.batch_size)
logging.info('negative_adversarial_sampling = %d' %
args.negative_adversarial_sampling)
logging.info('hidden_dim = %d' % args.hidden_dim)
logging.info('gamma = %f' % args.gamma)
logging.info('negative_adversarial_sampling = %s' %
str(args.negative_adversarial_sampling))
if args.negative_adversarial_sampling:
logging.info('adversarial_temperature = %f' %
args.adversarial_temperature)
# Set valid dataloader as it would be evaluated during training
if args.do_train:
logging.info('learning_rate = %d' % current_learning_rate)
training_logs = []
#Training Loop
for step in range(init_step, args.max_steps):
log = kge_model.train_step(kge_model, optimizer, train_iterator,
args)
training_logs.append(log)
if step >= warm_up_steps:
current_learning_rate = current_learning_rate / 10
logging.info('Change learning_rate to %f at step %d' %
(current_learning_rate, step))
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
kge_model.parameters()),
lr=current_learning_rate)
warm_up_steps = warm_up_steps * 3
if step % args.save_checkpoint_steps == 0:
save_variable_list = {
'step': step,
'current_learning_rate': current_learning_rate,
'warm_up_steps': warm_up_steps
}
save_model(kge_model, optimizer, save_variable_list, args)
if step % args.log_steps == 0:
metrics = {}
for metric in training_logs[0].keys():
metrics[metric] = sum(
[log[metric]
for log in training_logs]) / len(training_logs)
log_metrics('Training average', step, metrics)
training_logs = []
if args.do_valid and step % args.valid_steps == 0:
logging.info('Evaluating on Valid Dataset...')
metrics = kge_model.test_step(kge_model, valid_triples,
all_true_triples, args)
log_metrics('Valid', step, metrics)
save_variable_list = {
'step': step,
'current_learning_rate': current_learning_rate,
'warm_up_steps': warm_up_steps
}
save_model(kge_model, optimizer, save_variable_list, args)
if args.do_valid:
logging.info('Evaluating on Valid Dataset...')
metrics = kge_model.test_step(kge_model,
valid_triples,
all_true_triples,
args,
candidate_entities,
id2e=id2entity,
id2rel=id2relationship)
log_metrics('Valid', step, metrics)
# if args.do_test:
# logging.info('Evaluating on Test Dataset...')
# metrics = kge_model.test_step(kge_model, test_triples, all_true_triples, args)
# log_metrics('Test', step, metrics)
if args.do_test:
logging.info('Evaluating on Test Dataset...')
metrics = kge_model.test_step(kge_model,
test_triples,
all_true_triples,
args,
candidate_entities,
id2e=id2entity,
id2rel=id2relationship)
log_metrics('Test', step, metrics)
if args.evaluate_train:
logging.info('Evaluating on Training Dataset...')
metrics = kge_model.test_step(kge_model, train_triples,
all_true_triples, args)
log_metrics('Test', step, metrics)
PJ7 = Boolean('pj7')
PJ8 = Boolean('pj8')
PJ9 = Boolean('pj9')
PJ10 = Boolean('pj10')
PJ11 = Boolean('pj11')
PJ12 = Boolean('pj12')
PJ13 = Boolean('pj13')
PJ14 = Boolean('pj14')
PJ15 = Boolean('pj15')
PJ16 = Boolean('pj16')
PJ17 = Boolean('pj17')
PJ18 = Boolean('pj18')
PJ19 = Boolean('pj19')
# Create a new knowledge base
kb = KB()
# GENERAL INFORMATION ABOUT THE CARDS
# This adds information which cards are Jacks
kb.add_clause(J4)
kb.add_clause(J9)
kb.add_clause(J14)
kb.add_clause(J19)
# Add here whatever is needed for your strategy.
kb.add_clause(J0)
kb.add_clause(J5)
kb.add_clause(J10)
kb.add_clause(J15)
# DEFINITION OF THE STRATEGY
# Add clauses (This list is sufficient for this strategy)
# PJ is the strategy to play jacks first, so all we need to model is all x PJ(x) <-> J(x),
import sys
from kb import KB, Boolean, Integer, Constant
# Define our integer symbols
x = Integer('x')
y = Integer('y')
q = x == y
a = x + y > 2
b = x + y < 5
c = x + y < -2
d = x + y > -5
kb = KB()
kb.add_clause(q)
kb.add_clause(c, a)
kb.add_clause(d, a)
kb.add_clause(c, b)
kb.add_clause(d, b)
for model in kb.models():
print(model)
import sys
from kb import KB, Boolean, Integer, Constant
# Define our symbols
P = Boolean('P')
Q = Boolean('Q')
R = Boolean('R')
# Create P new knowledge base
kb = KB()
# KB
kb.add_clause(P, Q)
kb.add_clause(R, ~Q)
kb.add_clause(~P, ~R)
# ~alpha
kb.add_clause(P, ~Q)
kb.add_clause(~P, Q)
# Print all models of the knowledge base
for model in kb.models():
print(model)
# Print out whether the KB is satisfiable (if there are no models, it is not satisfiable)
print(kb.satisfiable())
import sys
from kb import KB, Boolean, Integer, Constant
# Define our integer symbols
x = Integer('x')
y = Integer('y')
q = x == y
a = x + y > 2
b = x + y < 5
c = x + y < -2
d = x + y > -5
kb = KB()
kb.add_clause(q)
kb.add_clause(a, c)
kb.add_clause(a, d)
kb.add_clause(b, c)
kb.add_clause(b, d)
for model in kb.models():
print(model)
# Print out whether the KB is satisfiable (if there are no models, it is not satisfiable)
print(kb.satisfiable())
Pa7 = Boolean('pa7')
Pa8 = Boolean('pa8')
Pa9 = Boolean('pa9')
Pa10 = Boolean('pa10')
Pa11 = Boolean('pa11')
Pa12 = Boolean('pa12')
Pa13 = Boolean('pa13')
Pa14 = Boolean('pa14')
Pa15 = Boolean('pa15')
Pa16 = Boolean('pa16')
Pa17 = Boolean('pa17')
Pa18 = Boolean('pa18')
Pa19 = Boolean('pa19')
# Create a new knowledge base
kb = KB()
# GENERAL INFORMATION ABOUT THE CARDS
# This adds information which cards are aacks
kb.add_clause(a0)
kb.add_clause(a5)
kb.add_clause(a10)
kb.add_clause(a15)
# Add here whatever is needed for your strategy.
# DEFINITION OF THE STRATEGY
# Add clauses (This list is sufficient for this strategy)
# Pa is the strategy to play aces first, so all we need to model is all x Pa(x) <-> a(x),
# In other words that the Pa strategy should play a card when it is an ace
kb.add_clause(~a0, Pa0)
kb.add_clause(~a5, Pa5)
import sys
from kb import KB, Boolean, Integer, Constant
# Define our integer symbols
x = Integer('x')
y = Integer('y')
q = x == y
a = x + y > 2
b = x + y < 5
c = x + y < -2
d = x + y > -5
kb = KB()
kb.add_clause(q)
kb.add_clause(a, d)
kb.add_clause(a, c)
kb.add_clause(b, d)
kb.add_clause(b, c)
for model in kb.models():
print(model)
PJ7 = Boolean('pj7')
PJ8 = Boolean('pj8')
PJ9 = Boolean('pj9')
PJ10 = Boolean('pj10')
PJ11 = Boolean('pj11')
PJ12 = Boolean('pj12')
PJ13 = Boolean('pj13')
PJ14 = Boolean('pj14')
PJ15 = Boolean('pj15')
PJ16 = Boolean('pj16')
PJ17 = Boolean('pj17')
PJ18 = Boolean('pj18')
PJ19 = Boolean('pj19')
# Create a new knowledge base
kb = KB()
# GENERAL INFORMATION ABOUT THE CARDS
# This adds information which cards are Jacks
kb.add_clause(J1)
kb.add_clause(J6)
kb.add_clause(J11)
kb.add_clause(J16)
# Add here whatever is needed for your strategy.
# DEFINITION OF THE STRATEGYcb
# Add clauses (This list is sufficient for this strategy)
# PJ is the strategy to play jacks first, so all we need to model is all x PJ(x) <-> J(x),
# In other words that the PJ strategy should play a card when it is a jack
kb.add_clause(~J1, PJ1)
kb.add_clause(~J6, PJ6)
PC14 = Boolean('pc14')
PC15 = Boolean('pc15')
PC16 = Boolean('pc16')
PC17 = Boolean('pc17')
PC18 = Boolean('pc18')
PC19 = Boolean('pc19')
#Trump suit
C = Boolean('c')
D = Boolean('d')
H = Boolean('h')
S = Boolean('s')
# Create a new knowledge base
kb = KB()
# GENERAL INFORMATION ABOUT THE CARDS
# This adds information which cards are Kings, Queens and Jacks
kb.add_clause(C2)
kb.add_clause(C3)
kb.add_clause(C4)
kb.add_clause(C7)
kb.add_clause(C8)
kb.add_clause(C9)
kb.add_clause(C12)
kb.add_clause(C13)
kb.add_clause(C14)
kb.add_clause(C17)
class ResourcePool(object):
default_model = "default"
def split_list(self, word_list):
# init
flag = 0
list_list_word = our_list = []
for i in word_list:
if i:
if i[0].startswith("#"):
if flag == 0:
flag = 1
if our_list:
list_list_word = list_list_word + [our_list]
our_list = []
else:
flag = 0
else:
if flag == 0:
our_list = our_list + [i]
list_list_word = list_list_word + [our_list]
return list_list_word
def __init__(self):
""" Empty constructor for the singleton decorator.
Real initialization must be manually triggered by calling ResourcePool.init().
"""
self.ontology_server = None
self.adjectives = {}
self.irregular_verbs_past = []
self.irregular_verbs_present = []
self.preposition_verbs = []
self.modal = []
self.adjective_verb = []
self.special_nouns = []
self.pronouns = []
self.numbers = []
self.demonstrative_det = []
self.adverbs = []
self.adverbs_at_end = []
self.proposals = []
self.preposition_rdf_object_property = {}
self.compelement_proposals = []
self.capital_letters = []
self.determinants = []
self.nouns_end_s = []
self.relatives = []
self.subsentences = []
self.det_quantifiers = []
self.adjective_rules = []
self.composed_nouns = []
self.plural_nouns = []
self.auxiliary = []
self.direct_transitive = []
self.indirect_transitive = []
self.state = []
self.complement_pronouns = []
self.concatenate_proposals = []
self.change_tuples = []
self.adjective_numbers = []
self.be_pronoun = []
self.noun_not_composed = []
self.adj_quantifiers = []
self.verb_need_to = []
self.prep_change_place = []
self.replace_tuples = []
self.adjective_numbers_digit = []
self.days_list = []
self.months_list = []
self.time_adverbs = []
self.location_adverbs = []
self.unusable_words = []
self.time_proposals = []
self.action_verb_with_passive_behaviour = {}
self.adjectives_ontology_classes = []
self.special_verbs = []
self.adv_sub = []
"""list of tokens that can start a sentence"""
self.sentence_starts = []
"""
list of verbs that express a goal - ie, that would translate to a
[S desires O] statement.
"""
self.goal_verbs = []
"""
dictionnary of all verbs for which thematic roles are known.
"""
self.thematic_roles = ThematicRolesDict()
def init(self, kb_host="localhost", kb_port=6969, embeddedkb=False, defaultontology=None, data_path=None):
if not data_path:
# try to guess the current prefix and then the data directory
data_path = os.path.abspath(__file__).split("lib")[0].split("src")[0] + "share/dialogs/"
logger.debug("Assuming Dialogs data dir is <%s>" % data_path)
try:
from kb import KB, KbError
except ImportError:
logger.error(
"Python bindings to access the knowledge are not available."
+ "Please install 'pykb' and restart Dialogs."
)
try:
self.ontology_server = KB(kb_host, kb_port, embeddedkb, defaultontology)
except KbError:
logger.error(
"Error while trying to connect to the knowledge base on %s:%s" % (kb_host, kb_port)
+ ". Continuing without knowledge base. Amongst others, resolution won't work."
)
for line in open(os.path.join(data_path, "adjectives")):
if line.startswith("#") or not line.strip():
continue
try:
adj, cat = line.split()
except ValueError: # for adjectives without category, set a generic "Feature" category
adj = line.split()[0]
cat = "Feature"
self.adjectives[adj] = cat
verbs = [list(line.split()) for line in open(os.path.join(data_path, "verbs"))]
verbs = self.split_list(verbs)
self.irregular_verbs_past = verbs[0]
self.irregular_verbs_present = verbs[1]
self.preposition_verbs = verbs[2]
self.modal = [k[0] for k in verbs[3]]
self.adjective_verb = [k[0] for k in verbs[4]]
self.auxiliary = [k[0] for k in verbs[5]]
self.direct_transitive = [k[0] for k in verbs[6]]
self.indirect_transitive = [k[0] for k in verbs[7]]
self.state = [k[0] for k in verbs[8]]
self.verb_need_to = [k[0] for k in verbs[9]]
self.special_verbs = [k[0] for k in verbs[12]]
# Action verbs such as 'see', 'hear' with no active behaviour
self.action_verb_with_passive_behaviour = dict([(k[0], k[1]) for k in verbs[10]])
self.goal_verbs = [k[0] for k in verbs[11]]
self.sentence_starts = [tuple(line.split()) for line in open(os.path.join(data_path, "sentence_starts"))]
nouns = [list(line.split()) for line in open(os.path.join(data_path, "nouns"))]
nouns = self.split_list(nouns)
self.special_nouns = [k[0] for k in nouns[0]]
self.pronouns = [k[0] for k in nouns[1]]
for i in nouns[1]:
if i[1] == "1":
self.complement_pronouns = self.complement_pronouns + [i[0]]
self.demonstrative_det = [k[0] for k in nouns[2]]
self.determinants = [k[0] for k in nouns[3]]
self.nouns_end_s = [k[0] for k in nouns[4]]
self.relatives = [k[0] for k in nouns[5]]
self.composed_nouns = [k[0] for k in nouns[6]]
self.plural_nouns = nouns[7]
self.noun_not_composed = [k[0] for k in nouns[8]]
self.days_list = nouns[9]
self.months_list = nouns[10]
self.unusable_words = [k[0] for k in nouns[11]]
# List of diection words, E.g: LEFT, RIGHT, TOP, etc ...
self.direction_words = [k[0] for k in nouns[12]]
self.compound_nouns = nouns[13]
###
### ADVERBIALS
adverbials = [list(line.split()) for line in open(os.path.join(data_path, "adverbial"))]
adverbials = self.split_list(adverbials)
self.adverbs = [k[0] for k in adverbials[0]]
self.time_adverbs = adverbials[0]
self.time_adverbs = [k[0] for k in adverbials[0] if k[1] in ["day", "hour"]]
self.time_adverbs += [k[0] for k in adverbials[1] if k[1] in ["day", "hour"]]
self.location_adverbs = [k[0] for k in adverbials[0] if k[1] == "location"]
self.location_adverbs += [k[0] for k in adverbials[1] if k[1] == "location"]
self.adverbs_at_end = [k[0] for k in adverbials[1]]
for k in adverbials[2]:
if k[1] == "1":
self.compelement_proposals = self.compelement_proposals + [k[0]]
self.proposals = [k[0] for k in adverbials[2]]
# Preposition with an existing object_property
# E.g: next+to => isNextTo
self.preposition_rdf_object_property = dict([(k[0], k[3:]) for k in adverbials[2]])
self.time_proposals = adverbials[2]
self.subsentences = [k[0] for k in adverbials[3]]
for k in adverbials[3]:
if k[1] == "1":
self.adv_sub = self.adv_sub + [k[0]]
self.prep_change_place = [k[0] for k in adverbials[4]]
grammatical_rules = [list(line.split()) for line in open(os.path.join(data_path, "grammatical_rules"))]
grammatical_rules = self.split_list(grammatical_rules)
self.numbers = grammatical_rules[0]
self.det_quantifiers = grammatical_rules[1]
self.capital_letters = [k[0] for k in grammatical_rules[2]]
self.adjective_rules = [k[0] for k in grammatical_rules[3]]
self.concatenate_proposals = grammatical_rules[4]
self.change_tuples = grammatical_rules[5]
self.adjective_numbers_digit = grammatical_rules[6]
self.adjective_numbers = [k[0] for k in grammatical_rules[6]]
self.be_pronoun = [k[0] for k in grammatical_rules[7]]
self.adj_quantifiers = [k[0] for k in grammatical_rules[8]]
for k in grammatical_rules[9]:
self.replace_tuples = self.replace_tuples + [[k[0], k[1:]]]
desc = ""
for line in open(os.path.join(data_path, "thematic_roles")):
if line.startswith("#") or not line.strip():
continue
desc += line
if line.startswith("}"): # end of block
self.thematic_roles.add_verb(desc)
desc = ""
# Add action verbs to the ontology
if self.ontology_server:
stmts = [
verb.capitalize() + " rdfs:subClassOf cyc:PurposefulAction"
for verb in self.thematic_roles.verbs.keys()
if not self.thematic_roles.verbs[verb].is_synonym()
]
self.ontology_server.revise(stmts, {"method": "add"})
"""
List of ontology classes that are used in the adjectives list
"""
self.adjectives_ontology_classes = [self.adjectives[adj].lower() for adj in self.adjectives]
adj_s = []
for k in self.adjectives_ontology_classes:
if not k in adj_s:
adj_s.append(k)
self.adjectives_ontology_classes = adj_s
def mark_active(self, ids):
if not self.ontology_server:
return
if isinstance(ids, basestring):
ids = [ids]
try:
self.ontology_server.revise(
[id + " rdf:type ActiveConcept" for id in ids],
{"method": "add", "models": [ResourcePool().default_model], "lifespan": 10},
)
except AttributeError:
# No ontology server
pass
def __del__(self):
self.close()
def close(self):
if self.ontology_server:
self.ontology_server.close()
self.ontology_server = None
import sys
from kb import KB, Boolean, Integer, Constant
# Define our symbols
A = Boolean('A')
B = Boolean('B')
C = Boolean('C')
D = Boolean('D')
# Create a new knowledge base
kb = KB()
# Add clauses
# kb.add_clause(A, B, C)
# kb.add_clause(~A, B)
# kb.add_clause(C)
# kb.add_clause(C)
# # Question 3
# kb.add_clause(A)
# kb.add_clause(C)
# kb.add_clause(D)
# kb.add_clause(~A)
# Question 4
kb.add_clause(A, B)
kb.add_clause(B, ~C)
kb.add_clause(~C, ~A)
# This needs to be unsatisfiable for entailment
kb.add_clause(A, ~B)
def init(self, kb_host="localhost", kb_port=6969, embeddedkb=False, defaultontology=None, data_path=None):
if not data_path:
# try to guess the current prefix and then the data directory
data_path = os.path.abspath(__file__).split("lib")[0].split("src")[0] + "share/dialogs/"
logger.debug("Assuming Dialogs data dir is <%s>" % data_path)
try:
from kb import KB, KbError
except ImportError:
logger.error(
"Python bindings to access the knowledge are not available."
+ "Please install 'pykb' and restart Dialogs."
)
try:
self.ontology_server = KB(kb_host, kb_port, embeddedkb, defaultontology)
except KbError:
logger.error(
"Error while trying to connect to the knowledge base on %s:%s" % (kb_host, kb_port)
+ ". Continuing without knowledge base. Amongst others, resolution won't work."
)
for line in open(os.path.join(data_path, "adjectives")):
if line.startswith("#") or not line.strip():
continue
try:
adj, cat = line.split()
except ValueError: # for adjectives without category, set a generic "Feature" category
adj = line.split()[0]
cat = "Feature"
self.adjectives[adj] = cat
verbs = [list(line.split()) for line in open(os.path.join(data_path, "verbs"))]
verbs = self.split_list(verbs)
self.irregular_verbs_past = verbs[0]
self.irregular_verbs_present = verbs[1]
self.preposition_verbs = verbs[2]
self.modal = [k[0] for k in verbs[3]]
self.adjective_verb = [k[0] for k in verbs[4]]
self.auxiliary = [k[0] for k in verbs[5]]
self.direct_transitive = [k[0] for k in verbs[6]]
self.indirect_transitive = [k[0] for k in verbs[7]]
self.state = [k[0] for k in verbs[8]]
self.verb_need_to = [k[0] for k in verbs[9]]
self.special_verbs = [k[0] for k in verbs[12]]
# Action verbs such as 'see', 'hear' with no active behaviour
self.action_verb_with_passive_behaviour = dict([(k[0], k[1]) for k in verbs[10]])
self.goal_verbs = [k[0] for k in verbs[11]]
self.sentence_starts = [tuple(line.split()) for line in open(os.path.join(data_path, "sentence_starts"))]
nouns = [list(line.split()) for line in open(os.path.join(data_path, "nouns"))]
nouns = self.split_list(nouns)
self.special_nouns = [k[0] for k in nouns[0]]
self.pronouns = [k[0] for k in nouns[1]]
for i in nouns[1]:
if i[1] == "1":
self.complement_pronouns = self.complement_pronouns + [i[0]]
self.demonstrative_det = [k[0] for k in nouns[2]]
self.determinants = [k[0] for k in nouns[3]]
self.nouns_end_s = [k[0] for k in nouns[4]]
self.relatives = [k[0] for k in nouns[5]]
self.composed_nouns = [k[0] for k in nouns[6]]
self.plural_nouns = nouns[7]
self.noun_not_composed = [k[0] for k in nouns[8]]
self.days_list = nouns[9]
self.months_list = nouns[10]
self.unusable_words = [k[0] for k in nouns[11]]
# List of diection words, E.g: LEFT, RIGHT, TOP, etc ...
self.direction_words = [k[0] for k in nouns[12]]
self.compound_nouns = nouns[13]
###
### ADVERBIALS
adverbials = [list(line.split()) for line in open(os.path.join(data_path, "adverbial"))]
adverbials = self.split_list(adverbials)
self.adverbs = [k[0] for k in adverbials[0]]
self.time_adverbs = adverbials[0]
self.time_adverbs = [k[0] for k in adverbials[0] if k[1] in ["day", "hour"]]
self.time_adverbs += [k[0] for k in adverbials[1] if k[1] in ["day", "hour"]]
self.location_adverbs = [k[0] for k in adverbials[0] if k[1] == "location"]
self.location_adverbs += [k[0] for k in adverbials[1] if k[1] == "location"]
self.adverbs_at_end = [k[0] for k in adverbials[1]]
for k in adverbials[2]:
if k[1] == "1":
self.compelement_proposals = self.compelement_proposals + [k[0]]
self.proposals = [k[0] for k in adverbials[2]]
# Preposition with an existing object_property
# E.g: next+to => isNextTo
self.preposition_rdf_object_property = dict([(k[0], k[3:]) for k in adverbials[2]])
self.time_proposals = adverbials[2]
self.subsentences = [k[0] for k in adverbials[3]]
for k in adverbials[3]:
if k[1] == "1":
self.adv_sub = self.adv_sub + [k[0]]
self.prep_change_place = [k[0] for k in adverbials[4]]
grammatical_rules = [list(line.split()) for line in open(os.path.join(data_path, "grammatical_rules"))]
grammatical_rules = self.split_list(grammatical_rules)
self.numbers = grammatical_rules[0]
self.det_quantifiers = grammatical_rules[1]
self.capital_letters = [k[0] for k in grammatical_rules[2]]
self.adjective_rules = [k[0] for k in grammatical_rules[3]]
self.concatenate_proposals = grammatical_rules[4]
self.change_tuples = grammatical_rules[5]
self.adjective_numbers_digit = grammatical_rules[6]
self.adjective_numbers = [k[0] for k in grammatical_rules[6]]
self.be_pronoun = [k[0] for k in grammatical_rules[7]]
self.adj_quantifiers = [k[0] for k in grammatical_rules[8]]
for k in grammatical_rules[9]:
self.replace_tuples = self.replace_tuples + [[k[0], k[1:]]]
desc = ""
for line in open(os.path.join(data_path, "thematic_roles")):
if line.startswith("#") or not line.strip():
continue
desc += line
if line.startswith("}"): # end of block
self.thematic_roles.add_verb(desc)
desc = ""
# Add action verbs to the ontology
if self.ontology_server:
stmts = [
verb.capitalize() + " rdfs:subClassOf cyc:PurposefulAction"
for verb in self.thematic_roles.verbs.keys()
if not self.thematic_roles.verbs[verb].is_synonym()
]
self.ontology_server.revise(stmts, {"method": "add"})
"""
List of ontology classes that are used in the adjectives list
"""
self.adjectives_ontology_classes = [self.adjectives[adj].lower() for adj in self.adjectives]
adj_s = []
for k in self.adjectives_ontology_classes:
if not k in adj_s:
adj_s.append(k)
self.adjectives_ontology_classes = adj_s
from kb import KB # 在主程式中直接寫知識庫 code = "A<=B. B<=C&D. C<=E. D<=F. E. F. Z<=C&D&G." kb1 = KB() kb1.load(code) # 載入知識庫 kb1.forwardChaining() # 開始進行前向推論
import sys
from kb import KB, Boolean, Integer, Constant
# Define our symbols
A = Boolean('A')
B = Boolean('B')
C = Boolean('C')
# Create a new knowledge base
kb = KB()
# Add clauses
kb.add_clause(A, B, C)
kb.add_clause(~A, B)
kb.add_clause(~B, C)
kb.add_clause(B, ~C)
# Print all models of the knowledge base
for model in kb.models():
print(model)
# Print out whether the KB is satisfiable (if there are no models, it is not satisfiable)
print(kb.satisfiable())
class Tracker(object):
"""
This is the main program which gives a high-level view
of all the running subsystems. It connects camera input with
output in form of "actions" (such as keyboard shortcuts on the users behalf).
This is done by locating a hand in an image and detecting features,
like the number of fingers, and trying to match that data with a
known gesture.
"""
def __init__(self):
"""
Configuration
"""
# Camera settings
self.FRAME_WIDTH = 341
self.FRAME_HEIGHT = 256
self.flip_camera = True # Mirror image
self.camera = cv2.VideoCapture(1)
# ...you can also use a test video for input
#video = "/Users/matthiasendler/Code/snippets/python/tracker/final/assets/test_video/10.mov"
#self.camera = cv2.VideoCapture(video)
#self.skip_input(400) # Skip to an interesting part of the video
if not self.camera.isOpened():
print "couldn't load webcam"
return
#self.camera.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, self.FRAME_WIDTH)
#self.camera.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, self.FRAME_HEIGHT)
self.filters_dir = "filters/" # Filter settings in trackbar
self.filters_file = "filters_default"
# Load filter settings
current_config = self.filters_dir + self.filters_file
self.filters = Filters(current_config)
# No actions will be triggered in test mode
# (can be used to adjust settings at runtime)
self.test_mode = False
# Create a hand detector
# In fact, this is a wrapper for many detectors
# to increase detection confidence
self.detector = Detector(self.filters.config)
# Knowledge base for all detectors
self.kb = KB()
# Create gesture recognizer.
# A gesture consists of a motion and a hand state.
self.gesture = Gesture()
# The action module executes keyboard and mouse commands
self.action = Action()
# Show output of detectors
self.output = Output()
self.run()
def run(self):
"""
In each step: Read the input image and keys,
process it and react on it (e.g. with an action).
"""
while True:
img = self.get_input()
hand = self.process(img)
ref = self.action.get_reference_point()
self.output.show(img, hand, ref)
def process(self, img):
"""
Process input
"""
# Run detection
hand = self.detector.detect(img)
# Store result in knowledge base
self.kb.update(hand)
if not self.test_mode:
# Try to interprete as gesture
self.interprete(hand)
return hand
def interprete(self, hand):
"""
Try to interprete the input as a gesture
"""
self.gesture.add_hand(hand)
operation = self.gesture.detect_gesture()
self.action.execute(operation)
def get_input(self):
"""
Get input from camera and keyboard
"""
self.get_key()
_, img = self.camera.read()
img = cv2.resize(img, (self.FRAME_WIDTH, self.FRAME_HEIGHT))
if self.flip_camera:
img = cv2.flip(img, 1)
return img
def get_key(self):
"""
Read keyboard input
"""
key = cv2.waitKey(self.filters.config["wait_between_frames"])
if key == ord('+'):
# Reduce program speed
self.filters.config["wait_between_frames"] += 500
if key == ord('-'):
# Increase program speed
if self.filters.config["wait_between_frames"] >= 500:
self.filters.config["wait_between_frames"] -= 500
#if key == ord('s'):
# Save config
# self.filters.save()
if key == ord('r'):
# Reset all detectors
self.detector.reset()
self.action.reset()
if key == ord('d'):
# Make a screenshot
self.output.make_screenshot()
if key == ord('p') or key == ord(' '):
# Pause
cv2.waitKey()
if key == ord('t'):
# Test mode
self.test_mode = not self.test_mode
if key == ord('1'):
self.output.toggle_estimate()
if key == ord('2'):
self.output.toggle_detectors()
if key == ord('3'):
self.output.toggle_skin()
if key == ord('f'):
self.toggle_filters()
if key == 63235: # Right arrow
self.skip_input(20)
if key == 27 or key == ord('q'):
# Abort program on ESC, q or space
exit()
def toggle_filters(self):
"""
Load the next filter settings
"""
self.filters_file = self.next_filters_file()
current_config = self.filters_dir + self.filters_file
self.filters.set_config(current_config)
def next_filters_file(self):
"""
Get the next filter settings
"""
filters = listdir(self.filters_dir)
for i, f in enumerate(filters):
if f == self.filters_file:
return filters[(i + 1) % len(filters)]
def skip_input(self, x=1):
"""
Skip to a different part of a video sequence.
"""
for i in range(0, x):
self.camera.grab()
'-p',
type=str,
default='',
help='file name for loading a saved knowledge base')
parser.add_argument('--save_kb',
'-s',
type=str,
default='',
help='file name to save the knowledge base to')
parser.add_argument('--responses',
'-r',
type=str,
default='',
help='file name for stored agent responses')
parser.add_argument('--output',
'-o',
type=str,
default='',
help='file name for output')
args = parser.parse_args()
kb = KB(args.responses)
print(
'Input looks like:\n\tA(n) <word1> is a(n) <word2>.\n\tA(n) <word1> is not a(n) <word2>.\n'
+
'\t<proper noun> is a <word1>.\n\t<proper noun> is not a <word1>.\n\t'
+
'Is a(n) <word1> a(n) <word2>?\n\tIs <proper noun> a <word1>?\n\tIs a <word1> <proper noun>?'
)
print('Type \'quit\' or \'q\' to exit.\n')
Shell(kb).start(args.input, args.prior_kb, args.save_kb, args.output)
def kb_consistent_trumpmarriage(self, state, move):
# type: (State, move) -> bool
# each time we check for consistency we initialise a new knowledge-base
kb = KB()
# Add general information about the game
suit = State.get_trump_suit(state)
if suit == "C":
card1 = 2
card2 = 3
variable_string = "m" + str(card1) + str(card2)
strategy_variable = Boolean(variable_string)
kb.add_clause(strategy_variable)
elif suit == "D":
card1 = 7
card2 = 8
variable_string = "m" + str(card1) + str(card2)
strategy_variable = Boolean(variable_string)
kb.add_clause(strategy_variable)
elif suit == "H":
card1 = 12
card2 = 13
variable_string = "m" + str(card1) + str(card2)
strategy_variable = Boolean(variable_string)
kb.add_clause(strategy_variable)
elif suit == "S":
card1 = 17
card2 = 18
variable_string = "m" + str(card1) + str(card2)
strategy_variable = Boolean(variable_string)
kb.add_clause(strategy_variable)
load.general_information(kb)
# Add the necessary knowledge about the strategy
load.strategy_knowledge(kb)
# This line stores the index of the card in the deck.
# If this doesn't make sense, refer to _deck.py for the card index mapping
index = move[0]
variable_string = "pm" + str(index)
strategy_variable = Boolean(variable_string)
# Add the relevant clause to the loaded knowledge base
kb.add_clause(~strategy_variable)
# If the knowledge base is not satisfiable, the strategy variable is
# entailed (proof by refutation)
return kb.satisfiable()
import sys
from kb import KB, Boolean, Integer, Constant
# Define our symbols
A = Boolean('A')
B = Boolean('B')
C = Boolean('C')
D = Boolean('D')
# Create a new knowledge base
kb = KB()
# Add clauses
kb.add_clause(A, B)
kb.add_clause(~B, A)
kb.add_clause(~A, C)
kb.add_clause(~A, D)
kb.add_clause(~A, ~C, ~D)
# Print all models of the knowledge base
for model in kb.models():
print(model)
# Print out whether the KB is satisfiable (if there are no models, it is not satisfiable)
print(kb.satisfiable())
import sys
from kb import KB
kb1 = KB()
with open(sys.argv[1], encoding='utf-8') as file:
code = file.read().replace(r'\n', '')
kb1.load(code)
kb1.forwardChaining()
while True:
line = input('?-')
if line == 'exit':
break
kb1.addFact(line.strip())
kb1.forwardChaining()
PC7 = Boolean('pc7')
PC8 = Boolean('pc8')
PC9 = Boolean('pc9')
PC10 = Boolean('pc10')
PC11 = Boolean('pc11')
PC12 = Boolean('pc12')
PC13 = Boolean('pc13')
PC14 = Boolean('pc14')
PC15 = Boolean('pc15')
PC16 = Boolean('pc16')
PC17 = Boolean('pc17')
PC18 = Boolean('pc18')
PC19 = Boolean('pc19')
# Create a new knowledge base
kb = KB()
# strategy: plays cheap card first (J, Q, K)
# This adds information which cards are Cheap
kb.add_clause(C2)
kb.add_clause(C3)
kb.add_clause(C4)
kb.add_clause(C7)
kb.add_clause(C8)
kb.add_clause(C9)
kb.add_clause(C12)
kb.add_clause(C13)
kb.add_clause(C14)
kb.add_clause(C17)
kb.add_clause(C18)
kb.add_clause(C19)