def simulate_number_of_words(nr_words):
"""
Simulations with a varying number of responses to a RAT problem.
The simulation is run for the maximal number of words and based on that the
success rates for a smaller number of words are calculated.
"""
performance = np.zeros((len(nr_words), 4))
max_words = nr_words[-1]
# load an existing file
try:
filename = 'nr_words_simulation.npz'
positions = np.load(filename)['positions']
print('Loading existing file...')
except IOError:
print('Running simulation!')
positions, _ = simulate_test(**{'nr_words': max_words})
np.savez(filename, positions=positions)
nr_items = float(len(positions))
for i, nr_w in enumerate(nr_words):
print('Testing words = %d' % nr_w)
solutions = np.where(positions < nr_w, positions, -1)
performance[i, :3] = get_difficulties(solutions)
percent_correct = 100 * len(np.where(solutions > -1)[0]) / nr_items
performance[i, 3] = percent_correct
return performance
def simulate_number_of_words(nr_words):
"""
Simulations with a varying number of responses to a RAT problem.
The simulation is run for the maximal number of words and based on that the
success rates for a smaller number of words are calculated.
"""
performance = np.zeros((len(nr_words), 4))
max_words = nr_words[-1]
# load an existing file
try:
filename = 'nr_words_simulation.npz'
positions = np.load(filename)['positions']
print('Loading existing file...')
except IOError:
print('Running simulation!')
positions, _ = simulate_test(**{'nr_words': max_words})
np.savez(filename, positions=positions)
nr_items = float(len(positions))
for i, nr_w in enumerate(nr_words):
print('Testing words = %d' % nr_w)
solutions = np.where(positions < nr_w, positions, -1)
performance[i, :3] = get_difficulties(solutions)
percent_correct = 100*len(np.where(solutions > -1)[0])/nr_items
performance[i, 3] = percent_correct
return performance
def simulate_threshold(thresholds, nr_words):
"""
Simulations with different thresholds.
"""
performance = np.zeros((len(thresholds), 4))
for i, th in enumerate(thresholds):
print('Threshold = %.2f' % th)
param = {'theta': th, 'nr_words': nr_words}
positions, _ = simulate_test(**param)
nr_items = len(positions)
performance[i, :3] = get_difficulties(positions)
percent_correct = 100 * len(np.where(positions > -1)[0]) / nr_items
performance[i, 3] = percent_correct
return performance
def simulate_threshold(thresholds, nr_words):
"""
Simulations with different thresholds.
"""
performance = np.zeros((len(thresholds), 4))
for i, th in enumerate(thresholds):
print('Threshold = %.2f' % th)
param = {'theta': th,
'nr_words': nr_words
}
positions, _ = simulate_test(**param)
nr_items = len(positions)
performance[i, :3] = get_difficulties(positions)
percent_correct = 100*len(np.where(positions > -1)[0])/nr_items
performance[i, 3] = percent_correct
return performance
for i, sid in enumerate(cor_wr):
c = 'g'
if sid == 0:
c = 'r'
pl.plot(i, sid, 'o', color=c)
pl.ylim([-.5, 1.5])
pl.xlabel('RAT Task ID')
pl.ylabel('Correct (1) / Wrong (0)')
print 'Performance:', success, '/', total
print 'Success rate:', rate
print 'Max position', solution_pos.max()
pl.show()
if __name__ == "__main__":
param = {'nr_words': 5,
'theta': 0.0
}
positions, responses = simulate_test(**param)
easy, mid, hard = get_difficulties(positions)
print 'Easy:', 100*easy
print 'Mid:', 100*mid
print 'Hard:', 100*hard
# plot_statistics(positions)
