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 plot_thesis(self):
w_hf, w_lf, prob_correct_hf, prob_correct_lf = self.get_results()
plots.plt_sum(w_hf, -0.5, 4) # Figure 4.3 panel A in thesis
plt.show()
plots.plt_sum(w_lf, -0.1, 1.6) # Figure 4.3 panel B in thesis
plt.show()
plots.plt_luce(prob_correct_hf) # Figure 4.3 panel C in thesis
plt.show()
plots.plt_luce(prob_correct_lf) # Figure 4.3 panel D in thesis
plt.show()
def exp3_raw_weights_lf():
test_features = [1, 3]
test_trials = [6999]
w_lf = exp3_test_raw(test_features, test_trials)
return w_lf
if __name__ == '__main__':
exp3_weights_fig()
w_hf = exp3_raw_weights_hf()
w_lf = exp3_raw_weights_lf()
# Experiment 3 plot sum of raw weights at test: Figure 4.3 panel A in thesis
plots.plt_sum(w_hf, -0.1, 1.6)
plt.show()
# Experiment 3 plot Luce's choice axiom results: Figure 4.3 panel C in thesis
prob_correct_hf = testmodel.luces_choice(w_hf)
plots.plt_luce(prob_correct_hf)
plt.show()
# Experiment 3 plot sum of raw weights at test: Figure 4.3 panel B in thesis
plots.plt_sum(w_lf, -0.1, 1.6)
plt.show()
# Experiment 3 plot Luce's choice axiom results: Figure 4.3 panel D in thesis
prob_correct_lf = testmodel.luces_choice(w_lf)
plots.plt_luce(prob_correct_lf)
plt.show()
def exp1_luces_fig():
# Experiment 1 plot Luce's choice axiom results: Figure 3.3 panel B in thesis
prob_correct = testmodel.luces_choice(w)
plots.plt_luce(prob_correct)
plt.show()