def averaged_weights(weights_init, learning_set, probabilities, categories,
number_of_categories, condition, alpha, beta, n_trials):
"""Compute avrage weights over 500 runs of the model
Keyword arguments:
weights_init -- weights after a single run of the model
learning set -- trials on which the model is trained (array)
probabilities -- probabilities of each trial in learning_set (vector)
categories -- all categories (e.g., [0, 1] - affix1 and affix2) (vector)
number_of_categories -- the number of cateogries (affixes) (integer)
condition -- "suffix" or "prefix" (string)
alpha -- cue saliency (integer or array)
beta -- learning rate (integer)
n_trials -- number of trials (integer)
"""
i = 0
while i < 10:
if condition == "prefix":
network = Simulation(learning_set, probabilities, categories,
number_of_categories, "prefix", alpha, beta,
n_trials)
new_weights = network.simulate()
else:
network = Simulation(learning_set, probabilities, categories,
number_of_categories, "suffix", alpha, beta,
n_trials)
new_weights = network.simulate()
weights_init += new_weights
i += 1
weights_avg = (weights_init) / 10.0
return weights_avg
def exp1_follow_up():
alpha = exp1_alpha()
learning_set, probabilities, categories, number_of_categories = experiment_1.get_experiment1_setup(
)
beta = 0.1
n_trials = 2000
p = Simulation(learning_set, probabilities, categories,
number_of_categories, "prefix", alpha, beta, n_trials)
prefix_weights = p.simulate()
s = Simulation(learning_set, probabilities, categories,
number_of_categories, "suffix", alpha, beta, n_trials)
suffix_weights = s.simulate()
# Figure 5.1 in thesis
plots.exp1_plt_weights(prefix_weights, 1)
plots.exp1_plt_weights(suffix_weights, 2)
plt.show()
# Experiment 1 plot raw weights at test: Figure 5.2 panel A in thesis
test_features = [0]
test_trials = [49, 99, 499, 999, 1499, 1999]
labels = ["50", "100", "500", "1000", "1500", "2000"]
prefix_averaged_weights = testmodel.averaged_weights(
prefix_weights, learning_set, probabilities, categories,
number_of_categories, "prefix", alpha, beta, n_trials)
suffix_averaged_weights = testmodel.averaged_weights(
suffix_weights, learning_set, probabilities, categories,
number_of_categories, "suffix", alpha, beta, n_trials)
# Prefix
prefix_correct_weights = prefix_averaged_weights[:, 0]
prefix_incorrect_weights = prefix_averaged_weights[:, 1]
prefix_results = testmodel.sums(test_features, test_trials,
prefix_correct_weights,
prefix_incorrect_weights)
# Suffix
suffix_correct_weights = suffix_averaged_weights[:, 0]
suffix_incorrect_weights = suffix_averaged_weights[:, 1]
suffix_results = testmodel.sums(test_features, test_trials,
suffix_correct_weights,
suffix_incorrect_weights)
plots.plt_sum_intervals(prefix_results, 1, len(test_trials), labels)
plots.plt_sum_intervals(suffix_results, 2, len(test_trials), labels)
plt.show()
# Plot Luce's choice axiom results: Figure 5.2 panel B in thesis
prefix_500 = prefix_results[:, 2]
suffix_500 = suffix_results[:, 2]
sum_weights = [prefix_500, suffix_500]
sum_weights = np.transpose(np.stack(sum_weights))
prob_correct = testmodel.luces_choice(sum_weights)
plots.plt_luce(prob_correct)
plt.show()
def exp1_follow_up():
# alpha = exp1_alpha()
alpha = 0.1
learning_set, probabilities, categories, number_of_categories = get_experiment1_setup(
)
beta = 0.1
n_trials = 2000
p = Simulation(learning_set, probabilities, categories,
number_of_categories, "prefix", alpha, beta, n_trials)
prefix_weights = p.simulate()
s = Simulation(learning_set, probabilities, categories,
number_of_categories, "suffix", alpha, beta, n_trials)
suffix_weights = s.simulate()
# Figure 5.1 in thesis
plots.exp1_plt_weights(prefix_weights, 1)
plots.exp1_plt_weights(suffix_weights, 2)
plt.show()
from simulations import Simulation
import json
import sys
import numpy as np
import os
task_id = sys.argv[1]
task_id = int(task_id)-1
dest = '/jukebox/wang/deverett/simulations/batch_9'
with open('generation_params.json','r') as f:
params = json.loads(f.read())
vars = params['variables']
vals = params['values']
var_n = int(np.floor(float(task_id)/float(len(vals[0]))))
val_n = task_id % len(vals[0])
varr = vars[var_n]
val_list = vals[var_n]
val = val_list[val_n]
name = "%02d_%03d"%(var_n+1,val_n+1)
sim = Simulation(name)
setattr(sim,varr,val)
sim.generate_movie()
sim.save_mov(fmt='tif',dest=dest)
sim.save_data(fmt='npy', dest=dest)
#sim.save_data(fmt='mat', dest=dest)
probabilities = np.array([0.75, 0.25, 0.75, 0.25]) / 2.
categories = np.array([0, 0, 1, 1])
number_of_categories = 2
return (learning_set, probabilities, categories, number_of_categories)
learning_set, probabilities, categories, number_of_categories = get_experiment3_setup(
)
alpha = 0.1
beta = 0.1
n_trials = 7000
p = Simulation(learning_set, probabilities, categories, number_of_categories,
"prefix", alpha, beta, n_trials)
prefix_weights = p.simulate()
s = Simulation(learning_set, probabilities, categories, number_of_categories,
"suffix", alpha, beta, n_trials)
suffix_weights = s.simulate()
def exp3_test_raw(test_features, test_trials):
"""Test the model as follows:
Do a single run of the model.
Do an additional 499 runs and average the weights across runs (testmodel.averaged_weights).
For a test item, sum up the weights of its features for the correct and the incorerct affix (testmodel.test).
Return:
weights: for both conditions, the raw weight sum for the correct affix and the incorrect affix
import sys
import Image
import numpy as np
from sys import path
path.append('.')
from nanowebs import creeNanoWeb1
from nanowebs import creeNanoWeb2
from nanowebs import creeNanoWeb3
from simulations import Simulation
#Créer une ditribution de probabilité uniforme
def creeEstimation(taille):
pi_t = dict()
for i in range(taille):
pi_t[i] = 1.0 / float(taille)
return np.array(pi_t.values())
pi_t = np.array([0.08, 0.1, 0.1, 0.05, 0.3, 0.05, 0.01, 0.02, 0.09, 0.02])
nanoweb = creeNanoWeb3()
s = Simulation(nanoweb)
s.trace(10, "epsilon1.txt")
#s.estimate(10000,0.0001,pi_t)
s.estimate(10000, 0.0001, creeEstimation(len(nanoweb.matrice)))
s.showFrequencies()
from simulations import Simulation # this is for manually generating single simulations. for batches, use "generate_simulation*s*.py" dest = './output' name = 'high_npil_22' sim = Simulation(name) sim.imaging_noise = [0.,0.18] #0.1, 0.4 [making normal=0.18] sim.neuropil_mag = 2.2 #0.05, 2.2 [normal=1.0] sim.generate_movie() sim.save_mov(fmt='tif',dest=dest) sim.save_data(fmt='npy', dest=dest)
def train_model(self):
prefix_weights = Simulation(self.learning_set, self.probabilities, self.categories, self.number_of_categories,
"prefix", self.beta, self.n_trials).simulate()
suffix_weights = Simulation(self.learning_set, self.probabilities, self.categories, self.number_of_categories,
"suffix", self.beta, self.n_trials).simulate()
return prefix_weights, suffix_weights