def simulate(axis, shape, nsims, maxcats, xlim, ylim):
c_space = commcost.Space(shape)
r_space = rectlang.Space(shape)
for n_cats in range(2, maxcats + 1):
color = str(((maxcats + 2) - (n_cats - 1)) / (maxcats + 2))
X, Y = [], []
for i in range(nsims):
_, part = commcost.random_partition(shape, n_cats, True)
comp = r_space.complexity(part)
cost = c_space.cost(part)
if comp < xlim[1] and cost > ylim[0]:
X.append(comp)
Y.append(cost)
_, part = commcost.random_partition(shape, n_cats, False)
comp = r_space.complexity(part)
cost = c_space.cost(part)
if comp < xlim[1] and cost > ylim[0]:
X.append(comp)
Y.append(cost)
axis.scatter(X, Y, color=color)
return X, Y
def transform_paper3_data():
r_space = rectlang.Space((6, 8))
c_space = commcost.Space((6, 8))
old_new = {
0: (1, 3),
1: (0, 2),
2: (0, 6),
3: (2, 5),
4: (5, 1),
5: (3, 7),
6: (0, 5),
7: (5, 7),
8: (4, 2),
9: (5, 2),
10: (4, 0),
11: (0, 0),
12: (5, 6),
13: (4, 3),
14: (1, 4),
15: (4, 4),
16: (1, 1),
17: (4, 1),
18: (2, 4),
19: (5, 4),
20: (4, 5),
21: (3, 0),
22: (5, 5),
23: (3, 3),
24: (2, 6),
25: (3, 5),
26: (0, 4),
27: (0, 7),
28: (2, 1),
29: (3, 1),
30: (3, 4),
31: (3, 2),
32: (4, 6),
33: (1, 2),
34: (0, 1),
35: (3, 6),
36: (1, 7),
37: (1, 0),
38: (4, 7),
39: (2, 2),
40: (2, 7),
41: (2, 0),
42: (1, 5),
43: (1, 6),
44: (5, 0),
45: (5, 3),
46: (2, 3),
47: (0, 3)
}
data = {'chains': []}
for chain in ['I', 'J', 'K', 'L']:
chain_data = {'chain_id': chain, 'generations': []}
for generation in range(11):
lang = np.full((6, 8), -1, dtype=int)
words = {}
with open('/Users/jon/Code/flatlanders/data/experiment_3/%s/%is' %
(chain, generation)) as file:
for i, line in enumerate(file):
word = line.split('\t')[0]
if word in words:
words[word].append(i)
else:
words[word] = [i]
for cat, (key, values) in enumerate(words.items(), 0):
for value in values:
coord = old_new[value]
lang[coord] = cat
comp = r_space.complexity(lang)
cost = c_space.cost(lang)
chain_data['generations'].append({
'prod_cost': cost,
'prod_complexity': comp
})
data['chains'].append(chain_data)
print(data)
languages. This was not included in the paper; refer to my thesis instead. ''' import pickle import numpy as np import matplotlib.pyplot as plt import commcost import rectlang import tools import colors plt.rcParams['svg.fonttype'] = 'none' rectlang_space = rectlang.Space((8,8)) commcost_space = commcost.Space((8,8)) commcost_space_discrete = commcost.Space((8,8), gamma=99999) def save_results(array, filename): with open(filename, mode='wb') as file: pickle.dump(array, file) def restore_results(filename): with open(filename, mode='rb') as file: array = pickle.load(file) return array def sim_all_cats(n_sims, shape, convex=False): complexy_results = np.zeros((64, n_sims), dtype=float) commcost_results = np.zeros((64, n_sims), dtype=float) commcost_results_discrete = np.zeros((64, n_sims), dtype=float)
'''
Converts the raw results from chains.json into the same JSON format
used by the model. Produces similar output to model_process.py
'''
import json
import numpy as np
import rectlang
import commcost
import varofinf
import tools
shape = (8,8)
rectlang_space = rectlang.Space(shape, solutions_file='../data/8x8_solutions.json')
commcost_space = commcost.Space(shape)
def raw_model_results_to_json_files(input_file, output_file):
results = {'bottleneck':2, 'exposures':4, 'chains':[]}
data = tools.read_json_lines(input_file)
for chain in data:
chain_data = {'chain_id':chain['chain_id'], 'first_fixation':chain['first_fixation'], 'generations':[]}
for gen_i, generation in enumerate(chain['generations']):
generation_data = {'generation_number':gen_i, 'productions':generation['partition'], 'data_out':[]}
productions = np.array(generation['partition'], dtype=int).reshape((8,8))
for stim_i in generation['training_out']:
meaning = (stim_i // shape[0], stim_i % shape[1])
signal = int(productions[meaning])
generation_data['data_out'].append((meaning, signal))
generation_data['prod_expressivity'] = len(np.unique(productions))
generation_data['prod_cost'] = commcost_space.cost(productions)
def __init__(self,
shape=(8, 8),
maxcats=4,
prior='simplicity',
weight=1.0,
noise=0.05,
exposures=4,
mcmc_iterations=5000):
if not isinstance(shape, tuple) or len(shape) != 2:
raise ValueError(
'shape should be a tuple containing the height and width')
if not isinstance(shape[0], int) or shape[0] < 1:
raise ValueError('height must be positive integer')
if not isinstance(shape[1], int) or shape[1] < 1:
raise ValueError('width must be postive integer')
self._shape = shape
self._size = np.product(shape)
self._cells, self._rects = self._generate_cells_and_rects()
if not isinstance(maxcats, int) or maxcats < 1 or maxcats > self._size:
raise ValueError(
'Invalid maxcats: Should be int between 1 and %i' % self._size)
self._maxcats = maxcats
if prior == 'simplicity':
self._prior_grid = rectlang.Space(self._shape,
rotational_invariance,
max_exhaustive_size,
max_beam_width)
self._prior_func = self._prior_grid.complexity
elif prior == 'informativeness':
self._prior_grid = commcost.Space(self._shape, gamma=gamma, mu=mu)
self._prior_func = self._prior_grid.cost
elif not callable(prior):
raise ValueError(
'Invalid prior: Use \'simplicity\' or \'informativeness\', or pass a callable function'
)
else:
self._prior_func = prior
if not isinstance(weight, (float, int)):
raise ValueError('Invalid weight: Should be float or int')
self._weight = weight
if not isinstance(noise, float) or noise < 0 or noise > 1:
raise ValueError('Invalid noise: Should be float between 0 and 1')
self._noise = noise
if not isinstance(exposures, int) or exposures < 1:
raise ValueError('Invalid exposures: Should be int greater than 0')
self._exposures = exposures
if not isinstance(mcmc_iterations, int) or mcmc_iterations < 1000:
raise ValueError(
'Invalid mcmc_iterations: Should be int greater than or equal to 1000'
)
self._mcmc_iterations = mcmc_iterations
self._prob_correct = np.log2(1.0 - self._noise)
self._prob_incorrect = np.log2(self._noise / (self._maxcats - 1))
self.language = None
def __init__(self,
generations=10,
shape=(8, 8),
mincats=1,
maxcats=4,
prior='simplicity',
weight=1.0,
noise=0.05,
bottleneck=2,
exposures=4,
mcmc_iterations=5000):
if not isinstance(generations, int) or generations < 1:
raise ValueError(
'Invalid generations: Should be int greater than 0')
self._generations = generations
if not isinstance(shape, tuple) or len(shape) != 2:
raise ValueError(
'shape should be a tuple containing the height and width')
if not isinstance(shape[0], int) or shape[0] < 1:
raise ValueError('height must be positive integer')
if not isinstance(shape[1], int) or shape[1] < 1:
raise ValueError('width must be postive integer')
self._shape = shape
self._size = np.product(shape)
if not isinstance(mincats, int) or mincats < 1 or mincats > self._size:
raise ValueError(
'Invalid mincats: Should be int between 1 and %i' % self._size)
self._mincats = mincats
if not isinstance(
maxcats,
int) or maxcats < self._mincats or maxcats > self._size:
raise ValueError(
'Invalid maxcats: Should be int between %i and %i' %
(self._mincats, self._size))
self._maxcats = maxcats
self._rectlang_grid = rectlang.Space(self._shape,
rotational_invariance,
max_exhaustive_size,
max_beam_width)
self._commcost_grid = commcost.Space(self._shape, gamma=gamma, mu=mu)
if prior == 'simplicity':
self._prior_func = self._rectlang_grid.complexity
elif prior == 'informativeness':
self._prior_func = self._commcost_grid.cost
elif not callable(prior):
raise ValueError(
'Invalid prior: Use \'simplicity\' or \'informativeness\', or pass a callable function'
)
else:
self._prior_func = prior
self._prior = str(prior)
if not isinstance(weight, (float, int)):
raise ValueError('Invalid weight: Must be float or int')
self._weight = weight
if not isinstance(noise, float) or noise < 0 or noise > 1:
raise ValueError('Invalid noise: Must be float between 0 and 1')
self._noise = noise
if not isinstance(bottleneck, int) or bottleneck < 1 or bottleneck > 4:
raise ValueError(
'Invalid bottleneck: Should be int between 1 and 4')
self._bottleneck = bottleneck
self._segments = self._segment_space()
if not isinstance(exposures, int) or exposures < 1:
raise ValueError('Invalid exposures: Must be int greater than 0')
self._exposures = exposures
if not isinstance(mcmc_iterations, int) or mcmc_iterations < 1000:
raise ValueError(
'Invalid mcmc_iterations: Should be int greater than or equal to 1000'
)
self._mcmc_iterations = mcmc_iterations
self._model_parameters = {
'shape': self._shape,
'mincats': self._mincats,
'maxcats': self._maxcats,
'prior': self._prior,
'weight': self._weight,
'noise': self._noise,
'bottleneck': self._bottleneck,
'exposures': self._exposures,
'mcmc_iterations': self._mcmc_iterations
}
self.generations = []