# print 'Incrementing word - pos: %s, word: %s, lmk_class: %s' % (lhs, rhs, (lmk.object_class if lmk else None) )
update_word_counts( update, lhs, rhs, prev_word, lmk_class=(lmk.object_class if lmk else None),
rel=rel,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None) )
# this class is only used for the --location command line argument
class Point(object):
def __init__(self, s):
x, y = s.split(',')
self.xy = (float(x), float(y))
self.x, self.y = self.xy
def __repr__(self):
return 'Point(%s, %s)' % self.xy
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--location', type=Point, required=True)
parser.add_argument('--consistent', action='store_true')
args = parser.parse_args()
scene, speaker = construct_training_scene()
meaning, sentence = generate_sentence(args.location.xy, args.consistent, scene, speaker)
logger('Generated sentence: %s' % sentence)
correction = raw_input('Correction? ')
accept_correction( meaning, correction)
from table2d.landmark import Landmark, PointRepresentation
from nltk.metrics.distance import edit_distance
from planar import Vec2
from utils import logger
import numpy as np
from matplotlib import pyplot as plt
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-n', '--num_iterations', type=int, default=1)
parser.add_argument('-l', '--location', type=Point)
parser.add_argument('--consistent', action='store_true')
args = parser.parse_args()
scene, speaker = construct_training_scene()
table = scene.landmarks['table'].representation.get_geometry()
window = 10
scales = [100]
min_dists = []
max_dists = []
avg_min = []
max_mins = []
for iteration in range(args.num_iterations):
logger('Iteration %d' % iteration)
scale = 1000
rand_p = Vec2(random() * table.width + table.min_point.x,
random() * table.height + table.min_point.y)
meaning, sentence = generate_sentence(rand_p, args.consistent, scene,
that can be used to describe the given location"""
landmarks = self.landmarks
if num_ancestors is not None:
landmarks = [l for l in landmarks if l.get_ancestor_count() == num_ancestors]
loc = Landmark(None, PointRepresentation(loc), None, None)
lmk, lmk_prob, lmk_entropy, head_on = self.speaker.sample_landmark( landmarks, loc )
rel, rel_prob, rel_entropy = self.speaker.sample_relation(loc, self.table.representation.get_geometry(), head_on, lmk, step=0.5)
rel = rel(head_on,lmk,loc)
return (lmk, lmk_prob, lmk_entropy), (rel, rel_prob, rel_entropy)
# we will use this instance of the scene
scene = ModelScene( *construct_training_scene() )
# helper functions
def lmk_id(lmk):
if lmk: return scene.get_landmark_id(lmk)
def rel_type(rel):
if rel: return rel.__class__.__name__
def get_meaning(loc=None, num_ancestors=None):
if not loc:
loc = scene.get_rand_loc()
lmk, rel = scene.sample_lmk_rel(Vec2(*loc), num_ancestors)
that can be used to describe the given location"""
landmarks = self.landmarks
if num_ancestors is not None:
landmarks = [l for l in landmarks if l.get_ancestor_count() == num_ancestors]
loc = Landmark(None, PointRepresentation(loc), None, None)
lmk, lmk_prob, lmk_entropy, head_on = self.speaker.sample_landmark( landmarks, loc )
rel, rel_prob, rel_entropy = self.speaker.sample_relation(loc, self.table.representation.get_geometry(), head_on, lmk, step=0.5)
rel = rel(head_on,lmk,loc)
return (lmk, lmk_prob, lmk_entropy), (rel, rel_prob, rel_entropy)
# we will use this instance of the scene
scene = ModelScene( *construct_training_scene() )
# helper functions
def lmk_id(lmk):
if lmk: return scene.get_landmark_id(lmk)
def rel_type(rel):
if rel: return rel.__class__.__name__
def get_meaning(loc=None, num_ancestors=None):
if not loc:
loc = scene.get_rand_loc()
lmk, rel = scene.sample_lmk_rel(Vec2(*loc), num_ancestors)
