def multiple_prior_contexts():
experiment_name = 'multiple_prior_contexts'
directory = './' + experiment_name
mkdir_p(directory)
print()
print('experiment=\'%s\'' % (experiment_name))
e = SymbolsEncoder(
max_symbols=26, # maximum number of symbols
num_s=208) # number of statelets
sl = SequenceLearner(
num_spc=10, # number of statelets per column
num_dps=50, # number of coincidence detectors per statelet
num_rpd=12, # number of receptors per coincidence detector
d_thresh=6, # coincidence detector threshold
perm_thr=1, # receptor permanence threshold
perm_inc=1, # receptor permanence increment
perm_dec=0) # receptor permanence decrement
sl.input.add_child(e.output)
values = [
'a', 'z', 'a', 'z', 'a', 'z', 'b', 'z', 'b', 'z', 'b', 'z', 'c', 'z',
'c', 'z', 'c', 'z', 'd', 'z', 'd', 'z', 'd', 'z', 'e', 'z', 'e', 'z',
'e', 'z', 'f', 'z', 'f', 'z', 'f', 'z', 'g', 'z', 'g', 'z', 'g', 'z',
'h', 'z', 'h', 'z', 'h', 'z', 'i', 'z', 'i', 'z', 'i', 'z', 'j', 'z',
'j', 'z', 'j', 'z', 'k', 'z', 'k', 'z', 'k', 'z', 'l', 'z', 'l', 'z',
'l', 'z', 'm', 'z', 'm', 'z', 'm', 'z', 'n', 'z', 'n', 'z', 'n', 'z',
'o', 'z', 'o', 'z', 'o', 'z', 'p', 'z', 'p', 'z', 'p', 'z', 'q', 'z',
'q', 'z', 'q', 'z', 'r', 'z', 'r', 'z', 'r', 'z', 's', 'z', 's', 'z',
's', 'z', 't', 'z', 't', 'z', 't', 'z', 'u', 'z', 'u', 'z', 'u', 'z',
'v', 'z', 'v', 'z', 'v', 'z', 'w', 'z', 'w', 'z', 'w', 'z', 'x', 'z',
'x', 'z', 'x', 'z', 'y', 'z', 'y', 'z', 'y', 'z'
]
le = preprocessing.LabelEncoder()
le.fit(values)
int_values = le.transform(values)
scores = [0.0 for _ in range(len(values))]
count_s_acts = [0 for _ in range(len(values))]
count_s_hist = [0 for _ in range(len(values))]
count_cs = [0 for _ in range(len(values))]
hidden_s_usage = [0 for _ in range(2240)]
output_s_usage = [0 for _ in range(2240)]
print('val scr s_act s_his cs active output statelets')
for i in range(len(int_values)):
e.compute(value=int_values[i])
sl.compute(learn=True)
# update information
hidden_s_bits = sl.hidden.bits
hidden_s_acts = sl.hidden.acts
output_s_bits = sl.output.bits
output_s_acts = sl.output.acts
scores[i] = sl.get_score()
count_s_acts[i] = len(output_s_acts)
count_s_hist[i] = sl.get_historical_count()
count_cs[i] = sl.get_coincidence_set_count()
# update statelet usage
for s in range(len(output_s_usage)):
hidden_s_usage[s] += hidden_s_bits[s]
output_s_usage[s] += output_s_bits[s]
# plot statelets
if (i + 1) % 6 == 0:
title = values[i] + '_' + values[i - 1]
plot_statelets(directory, 'hidden_' + title, hidden_s_bits)
plot_statelets(directory, 'output_' + title, output_s_bits)
# print information
output_s_acts_str = '[' + ', '.join(
str(act).rjust(4) for act in output_s_acts) + ']'
print('{0:>3} {1:0.1f} {2:5d} {3:5d} {4:4d} {5:>4}'.format(
values[i], scores[i], count_s_acts[i], count_s_hist[i],
count_cs[i], output_s_acts_str))
# plot information
plot_results(directory, 'results', values, scores, count_s_acts,
count_s_hist, count_cs, 600, 2200)
plot_statelet_usage(directory, 'hidden', hidden_s_usage, 75)
plot_statelet_usage(directory, 'output', output_s_usage, 75)
'''
def three_events(statelet_snapshots_on=False):
experiment_name = 'three_events'
directory = './' + experiment_name
mkdir_p(directory)
print()
print('experiment=\'%s\'' % (experiment_name))
NUM_S = 208
NUM_SPC = 10
TOTAL_NUM_S = NUM_S * NUM_SPC
e = SymbolsEncoder(
max_symbols=26, # maximum number of symbols
num_s=NUM_S) # number of statelets
sl = SequenceLearner(
num_spc=NUM_SPC, # number of statelets per column
num_dps=50, # number of coincidence detectors per statelet
num_rpd=12, # number of receptors per coincidence detector
d_thresh=6, # coincidence detector threshold
perm_thr=1, # receptor permanence threshold
perm_inc=1, # receptor permanence increment
perm_dec=0) # receptor permanence decrement
sl.input.add_child(e.output)
values = [
'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'c', 'd', 'e',
'f', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'f', 'e', 'd',
'c', 'b', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'c',
'd', 'c', 'b', 'a', 'a', 'a', 'a', 'a'
]
le = preprocessing.LabelEncoder()
le.fit(values)
int_values = le.transform(values)
scores = [0.0 for _ in range(len(values))]
count_s_output_acts = [0 for _ in range(len(values))]
count_s_hidden_acts = [0 for _ in range(len(values))]
count_s_hist = [0 for _ in range(len(values))]
count_cs = [0 for _ in range(len(values))]
hidden_s_usage = [0 for _ in range(TOTAL_NUM_S)]
output_s_usage = [0 for _ in range(TOTAL_NUM_S)]
print('val scr s_act s_his cs active output statelets')
for i in range(len(int_values)):
e.compute(value=int_values[i])
sl.compute(learn=True)
# update information
hidden_s_bits = sl.hidden.bits
hidden_s_acts = sl.hidden.acts
output_s_bits = sl.output.bits
output_s_acts = sl.output.acts
scores[i] = sl.get_score()
count_s_output_acts[i] = len(output_s_acts)
count_s_hidden_acts[i] = len(hidden_s_acts)
count_s_hist[i] = sl.get_historical_count()
count_cs[i] = sl.get_coincidence_set_count()
# update statelet usage
for s in range(len(output_s_usage)):
hidden_s_usage[s] += hidden_s_bits[s]
output_s_usage[s] += output_s_bits[s]
# plot statelets
if statelet_snapshots_on and (i + 1) % 5 == 0 or i == 43:
title = 'step_' + str(i) + '_' + values[i] + '_' + values[i - 1]
plot_statelets(directory, 'hidden_' + title, hidden_s_bits)
plot_statelets(directory, 'output_' + title, output_s_bits)
# print information
output_s_acts_str = '[' + ', '.join(
str(act).rjust(4) for act in output_s_acts) + ']'
print('{0:>3} {1:0.1f} {2:5d} {3:5d} {4:4d} {5:>4}'.format(
values[i], scores[i], count_s_output_acts[i], count_s_hist[i],
count_cs[i], output_s_acts_str))
# plot information
plot_results(directory, 'results', values, scores, count_s_output_acts,
count_s_hidden_acts, count_s_hist, count_cs, 400, 400)
plot_statelet_usage(directory, 'hidden', hidden_s_usage, 75)
plot_statelet_usage(directory, 'output', output_s_usage, 75)
