def web_animations(dir_path):
for noise in ['0.01', '0.05', '0.1']:
for bottleneck in ['1', '2', '3', '4']:
for exposures in ['1', '2', '3', '4']:
input_file = '../data/model_sim/1.0_%s_%s_%s.json' % (
noise, bottleneck, exposures)
output_file = dir_path + 's_1.0_%s_%s_%s' % (noise, bottleneck,
exposures)
best_chain = il_results.extract_dataset(
tools.read_json_file(input_file), 0, 50, 'lang_cost', True)
il_animations.save_animation(best_chain,
output_file,
show_seen=False,
create_thumbnail=True)
il_animations.save_animation(best_chain,
output_file + '_seen',
show_seen=True,
create_thumbnail=False)
input_file = '../data/model_inf/1.0_%s_%s_%s.json' % (
noise, bottleneck, exposures)
output_file = dir_path + 'i_1.0_%s_%s_%s' % (noise, bottleneck,
exposures)
best_chain = il_results.extract_dataset(
tools.read_json_file(input_file), 0, 50, 'lang_cost', True)
il_animations.save_animation(best_chain,
output_file,
show_seen=False,
create_thumbnail=True)
il_animations.save_animation(best_chain,
output_file + '_seen',
show_seen=True,
create_thumbnail=False)
input_file = '../data/model_inf/500.0_%s_%s_%s.json' % (
noise, bottleneck, exposures)
output_file = dir_path + 'i_500.0_%s_%s_%s' % (
noise, bottleneck, exposures)
best_chain = il_results.extract_dataset(
tools.read_json_file(input_file), 0, 50, 'lang_cost', True)
il_animations.save_animation(best_chain,
output_file,
show_seen=False,
create_thumbnail=True)
il_animations.save_animation(best_chain,
output_file + '_seen',
show_seen=True,
create_thumbnail=False)
def extract_generation_distribution(data_path, measure, generation):
data = tools.read_json_file(data_path)
distribution = []
for chain in data['chains']:
datum = chain['generations'][generation][measure]
distribution.append(datum)
return distribution
def __init__(self, parameters=None):
ip = str(parameters['ip']) if 'ip' in parameters else DEFAULT_UDP_IP
port = int(
parameters['port']) if 'port' in parameters else DEFAULT_UDP_PORT
buffersize = int(
parameters['buffersize']
) if 'buffersize' in parameters else DEFAULT_UDP_BUFFERSIZE
self._blueprint = read_json_file(JSON_STRUCT_FILE)
self.udpserver = UDPServer(ip, port, buffersize)
def make_model_chains_figure(figure_path):
best_chain_sim = il_results.extract_dataset(
tools.read_json_file('../data/model_sim/1.0_0.01_2_2.json'), 0, 50,
'lang_cost', True)
best_chain_inf = il_results.extract_dataset(
tools.read_json_file('../data/model_inf/1.0_0.01_2_2.json'), 0, 50,
'lang_cost', True)
best_chain_strong_inf = il_results.extract_dataset(
tools.read_json_file('../data/model_inf/500.0_0.01_2_2.json'), 0, 50,
'lang_cost', True)
best_chain_sim['chain_id'] = 0
best_chain_inf['chain_id'] = 1
best_chain_strong_inf['chain_id'] = 2
data = {'chains': [best_chain_sim, best_chain_inf, best_chain_strong_inf]}
il_visualize.make_figure(data,
figure_path,
start_gen=0,
end_gen=50,
n_columns=17,
method='language',
rect_compress=True)
def load(data_path,
start_gen,
end_gen,
method='prod',
return_typical_chain=False):
data = tools.read_json_file(data_path)
dataset = {}
for measure in ['expressivity', 'complexity', 'cost', 'error']:
dataset[measure] = extract_dataset(data, start_gen, end_gen,
method + '_' + measure,
return_typical_chain)
return dataset
def main():
config = read_json_file("settings.json")
generate_database()
start_time = time.time()
save_global_pref('start_time', start_time)
vk_runner = VkRunner(config)
vk_runner.start()
tg_runner = TgRunner(config)
tg_runner.start()
pass
def generate_csv_for_stats(input_file, output_file, start_gen=1, end_gen=10): dataset = tools.read_json_file(input_file) csv = 'subject,chain,generation,expressivity,error,complexity,cost\n' subject = 1 for chain_i, chain in enumerate(dataset['chains'], 1): for gen_i in range(start_gen, end_gen+1): generation = chain['generations'][gen_i] expressivity = generation['prod_expressivity'] error = generation['prod_error'] complexity = generation['prod_complexity'] cost = generation['prod_cost'] csv += '%i,%i,%i,%i,%s,%s,%s\n' % (subject, chain_i, gen_i, expressivity, str(error), str(complexity), str(cost)) subject += 1 with open(output_file, mode='w') as file: file.write(csv)
def plot(datasets, shape, nsims, maxcats, figure_path, figsize=(5, 4.8)):
fig, axes = plt.subplots(len(datasets), 1, figsize=figsize, squeeze=False)
for (dataset, xlim, ylim), axis in zip(datasets, axes.flatten()):
plot_space(axis, dataset, shape, xlim, ylim, nsims, maxcats)
fig.tight_layout(pad=0.1, h_pad=0.5, w_pad=0.5)
fig.savefig(figure_path, format='svg')
tools.format_svg_labels(figure_path)
if not figure_path.endswith('.svg'):
tools.convert_svg(figure_path, figure_path)
if __name__ == '__main__':
paper2_experiment_2 = tools.read_json_file(
'../data/experiments/exp2_chains.json')
plot([(paper2_experiment_2, (0, 600), (4.25, 6.25))], (8, 8), 40, 8,
'/Users/jon/Desktop/simp_inf_space_paper2.eps', (5, 3.5))
paper3_experiment_1 = {
'chains': [{
'chain_id':
'A',
'first_fixation':
None,
'generations': [{
'prod_cost': 1.436063501088083,
'prod_complexity': 510.2091177638209
}, {
'prod_cost': 1.8013146362322552,
'prod_complexity': 500.3523506476383
def make_experiment_chains_figure(figure_path):
data = tools.read_json_file('../data/experiments/exp2_chains.json')
il_visualize.make_figure(data, figure_path, start_gen=0, end_gen=50, n_columns=17, rect_compress=True)
'J18': 2,
'J19': 2,
'J20': 2,
'J21': 2,
'J22': 2,
'K17': 3,
'K18': 3,
'K19': 3,
'L26': 2,
'L27': 2,
'L28': 2,
'L29': 2,
'L30': 2
}
results = tools.read_json_file('../data/experiments/exp2_chains.json')
chain_letters = 'ABCDEFGHIJKL'
participant_i = 1
for c, chain in enumerate(results['chains']):
for g, generation in enumerate(chain['generations']):
error = generation['prod_error']
if error is not None and error < 3.0: # exclude participants whose VI is greater than 3.0
data_in = [
tuple([tuple(meaning), signal])
for meaning, signal in chain['generations'][g - 1]['data_out']
]
data_out = [
tuple([tuple(meaning), signal])
for meaning, signal in np.ndenumerate(