def simulate_pulses(voltage, pulses, output_to_csv=False):
full_cycle_pulses = [x for x in range(1, pulses + 1)]
device = memristor.Memristor()
resistance = []
# Perform the experiment.
for _ in full_cycle_pulses:
device.apply_voltage(voltage)
resistance.append(device.read_resistance())
# Store the data into a data frame.
data = pd.DataFrame()
data["voltage"] = [voltage] * len(full_cycle_pulses)
data["resistance"] = resistance
data["pulse_number"] = full_cycle_pulses
if output_to_csv:
utility.save_data(data, "./output", "simulate-pulses")
# Plot the results.
ax = plt.subplots(figsize=(10, 7))[1]
data.plot(x="pulse_number", y="resistance", ax=ax)
plt.legend(data["voltage"].unique(), title="Voltage (V)")
plt.xlabel("Pulse number")
plt.ylabel("Resistance (Ω)")
plt.show()
def main():
revision = 1
print("Loading the classifier")
classifier = utility.load_model("train_rtext_rev{}".format(revision))
print("Reading in the training data")
train = utility.load_data("training", "rtext")
print("Predicting the rest of the training data")
pred = np.ravel(classifier.predict(list(train['rtext_bcat'])))
score = utility.rmsle_log(pred, train['votes_useful_log'])
print "Score:", score
print("Writing out new training data")
del train['rtext_bcat']
train['votes_useful_log_rtextpred_sgd'] = pd.Series(pred, index=train.index)
utility.save_data(train, "training", "rtext_sgd_rev{}".format(revision))
print("Reading in the test data")
test = utility.load_data("test", "rtext")
tepred = np.ravel(classifier.predict(list(test['rtext_bcat'])))
print("Writing out new test data")
del test['rtext_bcat']
test['votes_useful_log_rtextpred_sgd'] = pd.Series(tepred, index=test.index)
utility.save_data(test, "test", "rtext_sgd_rev{}".format(revision))
test['votes'] = pd.Series(np.exp(tepred) + 1, index=test.index)
print("Writing out a new submission file")
utility.write_submission(test, "rtextsgd_sub_rev{}".format(revision))
def do_scrape(
download_dir="download", download_processes_num=8, except_log_file_name="request_exceptions_discoverireland.log"
):
global img_dir
global processes_num
processes_num = download_processes_num
img_dir = site_subdir_creator(img_dir)(download_dir)
except_log_file = open(except_log_file_name, "w")
start_time = time.time()
discoverireland_data = process_site_layers(site_layers_description_list, except_log_file)
print "discoverireland.ie scrapping time: ", str(time.time() - start_time)
save_data(discoverireland_data, "discoverireland.dat")
except_log_file.close()
discoverireland_data = get_saved_data("discoverireland.dat")
to_csv(discoverireland_data)
def main():
revision = 4
print("Loading the classifier")
classifier = utility.load_model("train_rtext_rev{}".format(revision))
print("Reading in the training data")
train = utility.load_data("training", "rtext")
print("Predicting the rest of the training data")
bunch = 50000
pred = np.zeros(len(train))
for ibunch in range(int(len(train) / bunch)) :
beg = ibunch * bunch
end = (ibunch + 1) * 50000
mtrain = train.ix[beg:end - 1]
mpred = np.ravel(classifier.predict(list(mtrain['rtext_bcat'])))
pred[beg:end] = mpred
beg = int(len(train) / bunch) * bunch
mtrain = train.ix[beg:]
mpred = np.ravel(classifier.predict(list(mtrain['rtext_bcat'])))
pred[beg:] = mpred
score = utility.rmsle_log(pred, train['votes_useful_log'])
print "Score:", score
print("Writing out new training data")
del train['rtext_bcat']
train['votes_useful_log_rtextpred'] = pd.Series(pred, index=train.index)
utility.save_data(train, "training", "rtext_rev{}".format(revision))
print("Reading in the test data")
test = utility.load_data("test", "rtext")
tepred = np.ravel(classifier.predict(list(test['rtext_bcat'])))
print("Writing out new test data")
del test['rtext_bcat']
test['votes_useful_log_rtextpred'] = pd.Series(tepred, index=test.index)
utility.save_data(test, "test", "rtext_rev{}".format(revision))
test['votes'] = pd.Series(np.exp(tepred) + 1, index=test.index)
print("Writing out a new submission file")
utility.write_submission(test, "rtextrf_sub_rev{}.csv".format(revision))
def simulate_pulsing_experiment(output_to_csv=False):
half_cycle_pulses = 10
full_cycle_pulses = [x for x in range(1, half_cycle_pulses * 2 + 1)]
positive_bias = [x * 0.1 for x in range(10, 0, -1)]
negative_bias = -4
lrs_set_voltage = 1
device = memristor.Memristor()
resistance = []
# Perform the experiment.
for voltage in positive_bias:
device.set_resistance(lrs_set_voltage)
for _ in full_cycle_pulses[1: half_cycle_pulses + 1]:
device.apply_voltage(negative_bias)
resistance.append(device.read_resistance())
for _ in full_cycle_pulses[half_cycle_pulses:]:
device.apply_voltage(voltage)
resistance.append(device.read_resistance())
# Store the data into a data frame.
data = pd.DataFrame()
data["positive_bias"] = (positive_bias * len(full_cycle_pulses))
data.sort_values(by="positive_bias", ascending=False, inplace=True)
data.reset_index(inplace=True, drop=True)
data["resistance"] = resistance
data["pulse_number"] = full_cycle_pulses * len(positive_bias)
if output_to_csv:
utility.save_data(data, "./output", "simulate-pulsing-experiment")
# Plot the results.
ax = plt.subplots(figsize=(10, 7))[1]
data.groupby(["positive_bias"]).plot(x="pulse_number", y="resistance", ax=ax)
plt.legend(data["positive_bias"].unique()[::-1], title="Positive bias (V)")
plt.xlabel("Pulse number")
plt.ylabel("Resistance (Ω)")
plt.show()
def main():
trabus = utility.load_data("training", "business")
tesbus = utility.load_data("test", "business")
bus = pd.concat((trabus, tesbus))
for cat in delbuscats :
if hasattr(bus, cat) : del bus[cat]
bus['procbcat'] = pd.Series(map(process_bcat, bus['categories']), bus.index)
del bus['categories']
for s in ["training", "test"] :
rev = utility.load_data(s, "review")
for cat in delrevcats :
if hasattr(rev, cat) : del rev[cat]
if hasattr(rev, 'votes_useful') :
rev['votes_useful_log'] = np.log(rev.votes_useful + 1)
rev = pd.merge(rev, bus, 'inner')
rev['rtext_bcat'] = rev['text'] + rev['procbcat']
del rev['procbcat']
del rev['text']
utility.save_data(rev, s, 'rtext')
def save(self):
utility.save_data("insults", self.insults)
utility.save_data("insult_votes", self.votes)
def save_games(self):
utility.save_data("games", self.games)
def mask_save(self):
utility.save_data("urlmasks", self.url_masks)
def save_games(self):
utility.save_data("games", self.games)
def save(self):
utility.save_data("insults", self.insults)
def main():
usr = pd.concat((utility.load_data('training', 'user'),
utility.load_data('test', 'user')))
for cat in usrdelcats :
if hasattr(usr, cat) : del usr[cat]
usr = usr.rename(columns={'average_stars' : 'user_average_stars',
'review_count' : 'user_review_count'})
bus = pd.concat((utility.load_data('training', 'business'),
utility.load_data('test', 'business')))
for cat in busdelcats :
if hasattr(bus, cat) : del bus[cat]
bus = bus.rename(columns={'stars' : 'business_average_stars',
'review_count' : 'business_review_count',})
rtxttag = 'rtext_rev{}'.format(rtext_rev)
rtxt_tr = utility.load_data('training', rtxttag)
rtxt_te = utility.load_data('test', rtxttag)
rtxt_te.index = rtxt_te.index + len(rtxt_tr)
rtxt = pd.concat((rtxt_tr, rtxt_te))
sgdtag = 'rtext_sgd_rev{}'.format(rtext_sgd_rev)
sgdtxt_tr = utility.load_data('training', sgdtag)
sgdtxt_te = utility.load_data('test', sgdtag)
sgdtxt_te.index = sgdtxt_te.index + len(sgdtxt_tr)
sgdtxt = pd.concat((sgdtxt_tr, sgdtxt_te))
tesrev = utility.load_data('test', 'review')
trarev = utility.load_data('training', 'review')
revlist = [trarev, tesrev]
for i in range(len(revlist)) :
revlength = revlist[i]['text'].apply(lambda t : len(t.split()))
revlist[i]['review_length'] = revlength
for col in revlist[i].columns :
if not col in ['review_id', 'stars', 'date', 'review_length'] :
del revlist[i][col]
revlist[i] = revlist[i].rename(columns={'stars' : 'review_stars'})
revlist[i] = pd.merge(revlist[i], rtxt, 'left')
revlist[i] = pd.merge(revlist[i], sgdtxt, 'left')
revlist[i] = pd.merge(revlist[i], usr, 'left')
revlist[i] = pd.merge(revlist[i], bus, 'left')
revlist[i] = revlist[i].fillna(-1)
for c in normedcols : norm_col(revlist, c)
dates = [pd.to_datetime('2013-01-19'), pd.to_datetime('2013-03-12')]
for i in range(len(revlist)) :
ddiff = dates[i] - revlist[i]['date']
ddiff = ddiff.apply(lambda x: x / np.timedelta64(1, 'D'))
revlist[i]['datediff'] = ddiff
norm_col(revlist, 'datediff')
for i in range(len(revlist)) :
for c in delcols :
if hasattr(revlist[i], c) :
del revlist[i][c]
utility.save_data(revlist[0], 'training', 'finalinput')
utility.save_data(revlist[1], 'test', 'finalinput')
def set_location(self, value):
self.location = value
utility.save_data('festern_bbq', self.location)
global img_dir
global processes_num
processes_num = download_processes_num
img_dir = site_subdir_creator(img_dir)(download_dir)
except_log_file = open(except_log_file_name, "w")
start_time = time.time()
discoverireland_data = process_site_layers(site_layers_description_list, except_log_file)
print "discoverireland.ie scrapping time: ", str(time.time() - start_time)
save_data(discoverireland_data, "discoverireland.dat")
except_log_file.close()
discoverireland_data = get_saved_data("discoverireland.dat")
to_csv(discoverireland_data)
if __name__ == "__main__":
create_dirs(img_dir)
exceptions_log_file = open("discoverireland_exceptions.log", "w")
start_time = time.time()
discoverireland_data = process_site_layers(site_layers_description_list, exceptions_log_file)
print "discoverireland.ie scrapping time: ", str(time.time() - start_time)
save_data(discoverireland_data, "discoverireland.dat")
exceptions_log_file.close()
discoverireland_data = get_saved_data("discoverireland.dat")
to_csv(discoverireland_data)
# exceptions_log_file = open('discoverireland_exceptions.log', 'w')
# first_layer_processor(site_layers_description_list[0], exceptions_log_file)
# exceptions_log_file.close()
def set_notes(self, nick, text):
self.notebook[nick] = text
utility.save_data('notes', self.notebook)
def save(self):
utility.save_data("rss_watch_list", self.watch_list)
def solve_xor_snn(learning_pulses, epochs, learn_hidden_synapses=False, output_to_csv=False):
hrs_set_voltage = -4
positive_bias = 1
fixing_pulses = 37 * 10 ** 4
current_application_time = 500
spike = 1
true = 20
false = 0
training_voltage = 1
input_spikes_1 = [false, false, true, true]
input_spikes_2 = [false, true, false, true]
targets = [false, true, true, false]
# Create a network with 2 input neurons, 2 hidden layer neurons
# and 1 output neuron. The input neurons are simulated by their spikes.
hidden_neuron_1 = spiking_neuron.SpikingNeuron()
hidden_neuron_2 = spiking_neuron.SpikingNeuron()
output_neuron = spiking_neuron.SpikingNeuron()
# Create 6 synapses with input_synapse_ij representing the connection
# between the input neuron i and the hidden neuron j, and hidden_synapse_k
# representing the connection between the hidden neuron k and the output neuron.
# The inhibitory synapse is hidden_synapse_2.
input_synapse_11 = memristor.Memristor()
input_synapse_12 = memristor.Memristor()
input_synapse_21 = memristor.Memristor()
input_synapse_22 = memristor.Memristor()
hidden_synapse_1 = memristor.Memristor()
hidden_synapse_2 = memristor.Memristor()
# Set all the synapses to a high resistance state.
input_synapse_11.set_resistance(hrs_set_voltage)
input_synapse_12.set_resistance(hrs_set_voltage)
input_synapse_21.set_resistance(hrs_set_voltage)
input_synapse_22.set_resistance(hrs_set_voltage)
hidden_synapse_1.set_resistance(hrs_set_voltage)
hidden_synapse_2.set_resistance(hrs_set_voltage)
# If the resistance values at the hidden synapses are not going
# to be learned, then fix them by applying consecutive positive
# bias voltage pulses.
if not learn_hidden_synapses:
for _ in range(fixing_pulses):
hidden_synapse_1.apply_voltage(positive_bias)
hidden_synapse_2.apply_voltage(positive_bias)
c_11 = []
c_12 = []
c_21 = []
c_22 = []
hidden_spikes_1 = []
hidden_spikes_2 = []
z_1 = []
z_2 = []
output_spikes = []
errors = []
squared_errors = []
epoch_numbers = []
for epoch_number in range(1, epochs + 1):
for spikes_1, spikes_2, target in zip(input_spikes_1, input_spikes_2, targets):
epoch_numbers.append(epoch_number)
# The normalized current c_ij passes through the input_synapse_ij.
# The input voltage is 1 V if the input neuron fired as many pulses
# as the encoding of TRUE or more, else it is 0 V.
c_11.append(utility.normalize(utility.burst(spikes_1, true) / input_synapse_11.read_resistance()))
c_12.append(utility.normalize(utility.burst(spikes_1, true) / input_synapse_12.read_resistance()))
c_21.append(utility.normalize(utility.burst(spikes_2, true) / input_synapse_21.read_resistance()))
c_22.append(utility.normalize(utility.burst(spikes_2, true) / input_synapse_22.read_resistance()))
hidden_spikes_1.append(hidden_neuron_1.apply_current(c_11[-1] + c_21[-1],
current_application_time)[0].count(spike))
hidden_spikes_2.append(hidden_neuron_2.apply_current(c_12[-1] + c_22[-1],
current_application_time)[0].count(spike))
# The normalized current z_i passes through the hidden_synapse_i.
# The current in the inhibitory synapse is inverted. The input
# voltage is 1 V if the hidden neuron fired as many pulses as
# the encoding of TRUE or more, else it is 0 V.
z_1.append(utility.normalize(utility.burst(hidden_spikes_1[-1], true) / hidden_synapse_1.read_resistance()))
z_2.append(utility.invert(utility.normalize(utility.burst(hidden_spikes_2[-1], true)
/ hidden_synapse_2.read_resistance())))
output_spikes.append(output_neuron.apply_current(z_1[-1] + z_2[-1],
current_application_time)[0].count(spike))
errors.append(target - output_spikes[-1])
squared_errors.append(errors[-1] ** 2)
# Update the synapses based on the error.
# If the output neuron should have fired more times and it did not,
# then strengthen hidden synapse 1 if hidden neuron 1 fired as many
# pulses as the encoding of TRUE or more and if the resistance values
# at the hidden synapses are to be learned. Otherwise strengthen the
# input synapses of the input neurons that fired as many pulses as the
# encoding of TRUE or more and that are connected to hidden neuron 1.
if utility.is_false_negative(errors[-1]):
if utility.burst(hidden_spikes_1[-1], true) and learn_hidden_synapses:
for _ in range(learning_pulses):
hidden_synapse_1.apply_voltage(training_voltage)
else:
if utility.burst(spikes_1, true):
for _ in range(learning_pulses):
input_synapse_11.apply_voltage(training_voltage)
if utility.burst(spikes_2, true):
for _ in range(learning_pulses):
input_synapse_21.apply_voltage(training_voltage)
# If the output neuron should not have fired and it did, then do the
# equivalent update for hidden synapse 2 or for the input synapses that
# are connected to hidden neuron 2.
elif utility.is_false_positive(errors[-1]):
if utility.burst(hidden_spikes_2[-1], true) and learn_hidden_synapses:
for _ in range(learning_pulses):
hidden_synapse_2.apply_voltage(training_voltage)
else:
if utility.burst(spikes_1, true):
for _ in range(learning_pulses):
input_synapse_12.apply_voltage(training_voltage)
if utility.burst(spikes_2, true):
for _ in range(learning_pulses):
input_synapse_22.apply_voltage(training_voltage)
# The neurons are resting until the next input.
hidden_neuron_1.rest()
hidden_neuron_2.rest()
output_neuron.rest()
# Store the data into a data frame.
data = pd.DataFrame()
data["input_spikes_1"] = input_spikes_1 * epochs
data["input_spikes_2"] = input_spikes_2 * epochs
data["c_11"] = c_11
data["c_12"] = c_12
data["c_21"] = c_21
data["c_22"] = c_22
data["hidden_spikes_1"] = hidden_spikes_1
data["hidden_spikes_2"] = hidden_spikes_2
data["z_1"] = z_1
data["z_2"] = z_2
data["output_spikes"] = output_spikes
data["target"] = targets * epochs
data["error"] = errors
data["squared_error"] = squared_errors
data["epoch"] = epoch_numbers
data["learning_pulses"] = [learning_pulses] * epochs * 4
data["training_voltage"] = [training_voltage] * epochs * 4
data["learn_hidden_synapses"] = [learn_hidden_synapses] * epochs * 4
data["current_application_time"] = [current_application_time] * epochs * 4
if output_to_csv:
utility.save_data(data, "./output", "solve-xor-snn")
# Plot the MSE as a function of epoch.
utility.plot_mse(data)
def save(self):
utility.save_data("insults", self.insults)
utility.save_data("insult_votes", self.votes)
def measure_IP1(SMB_RF=SMB_RF,
NRP2=NRP2,
SAB=SAB,
synthetizer_state=synthetizer_state,
synthetizer_frequency=synthetizer_frequency,
synthetizer_fix_power=synthetizer_fix_power, # dBm
synthetizer_level=synthetizer_level, # dBm
power_meter_make_zero=power_meter_make_zero,
power_meter_make_zero_delay=power_meter_make_zero_delay,
power_meter_misure_number=power_meter_misure_number,
power_meter_misure_delay=power_meter_misure_delay, # ,milliseconds
SAB_state=SAB_state,
SAB_attenuation_level=SAB_attenuation_level,
SAB_attenuation_delay=SAB_attenuation_delay,
SAB_switch_01_delay=SAB_switch_01_delay,
SAB_switch_02_delay=SAB_switch_02_delay,
calibration_cable_file_name=calibration_cable_file_name,
result_file_name=result_file_name,
createprogressdialog=None
):
dialog = createprogressdialog
values = []
# open calibration file
calibration_LO = readcalibrationfile(calibration_cable_file_name)
calibration_function_LO, calibration_function_LO_unit = calibrationfilefunction(calibration_LO)
# reset the synthetizer SMB100A
SMB_RF.write("*rst")
# imposta la modalita'� di funzionamento del SMB100A
SMB_RF.write("FREQ:MODE FIX")
SMB_RF.write("POW:MODE FIX")
if str(SAB_state) == "OFF":
SAB_attenuation_level_min = 0
SAB_attenuation_level_max = 0
SAB_attenuation_level_step = 1
# synthetizer_frequency_min = unit.convertion_to_base(synthetizer_frequency_min, synthetizer_frequency_unit)
frequency_LO_range = synthetizer_frequency.return_range()
level_LO_range = synthetizer_level.return_arange()
attenuation_range = SAB_attenuation_level.return_arange()
maxcount = len(frequency_LO_range) * len(level_LO_range)
for s in attenuation_range:
count = 0
if SAB_state == True:
# set stepattenuator value
SAB.write("ATT " + str(s))
# print("Attenuation {att} dB".format(att = str(s)))
time.sleep(SAB_attenuation_delay)
s_value = str(s)
else:
s_value = "OFF"
for f in frequency_LO_range:
# set SMB100A frequency
if synthetizer_state == True:
f_value = str(f)
current_frequency = f_value + unit.return_unit_str(unit.Hz)
current_frequency_human_readable = unit.return_human_readable_str(f)
command = "FREQ " + current_frequency # Ex. FREQ 500kHz
SMB_RF.write(command)
for l in level_LO_range:
current_LO_level, calibration_LO_result = calibrate(l, f, unit.Hz, calibration_LO,
calibration_function=calibration_function_LO,
calibration_function_unit=calibration_function_LO_unit)
# set SMB100A level
# current_level = str(l)
current_level = str(current_LO_level)
command = "POW " + current_level
SMB_RF.write(command)
# turn on RF
if f == frequency_LO_range[0]:
SMB_RF.write("OUTP ON")
values.append([f_value, str(l), current_level, calibration_LO_result, s_value] + readNRP2(SAB, NRP2,
power_meter_misure_number,
power_meter_misure_delay,
f,
unit.Hz,
SAB_switch_01_delay,
make_zero=True))
count += 1
if not createprogressdialog is None:
import wx
wx.MicroSleep(500)
message = "RF {lo_freq} {lo_pow}dB".format(lo_freq=current_frequency_human_readable,
lo_pow=current_level)
newvalue = min([int(count / maxcount * 100), 100])
if newvalue == 100:
createprogressdialog = False
# dialog.Update(newvalue, message)
# wx.MicroSleep(500)
dialog.Close()
else:
dialog.Update(newvalue, message)
if f == frequency_LO_range[-1] and l == level_LO_range[-1] and s == attenuation_range[-1]:
# safety turn Off
# dialog.Update(100, "Measure completed)
dialog.Destroy()
else:
l = 0
f_value = "OFF"
current_level = "OFF"
calibration_LO_result = "OFF"
values.append([f_value, str(l), current_level, calibration_LO_result, s_value] + readNRP2(SAB, NRP2,
power_meter_misure_number,
power_meter_misure_delay,
f, unit.Hz,
SAB_switch_01_delay,
make_zero=True))
# if stepattenuator_state == "ON":
# #create step attenuator object
# STA.closeport()
# turn off RF
SMB_RF.write("OUTP OFF")
print("Misure completed\n")
save_data("txt", result_file_name, values, power_meter_misure_number, unit.return_unit_str(unit.Hz))
save_data("csv", result_file_name, values, power_meter_misure_number, unit.return_unit_str(unit.Hz))
return result_file_name
def save(self):
utility.save_data("places", self.places)
stats4 = player_data.find_all("div", {"class": "image-container-item"})
player_items = [stat_item.find("a").get('href').split("/")[2] for stat_item in stats4]
temp_player_dict["items"] = player_items
players_list.append(temp_player_dict)
else:
print("Not a known player for the match...")
if match_counter >= 3:
print(" {} People in party ".format(str(match_counter)))
match_dict[match] = players_list
# Not to get banned
time.sleep(2)
extraction_data["matches"] = match_dict
return extraction_data
else:
return
#print(main_process())
# Main execution
if __name__ == "__main__":
#print(main_process())
#Extract player data
player_infos = player_info()
# Save it as pickle
utility.save_data("player_data", player_infos)
#For loading the player infos
#utility.load_data("player_data")
def save(self):
utility.save_data("compliments", self.compliments)
# S = Society(delta=delta, beta=beta, tau=0.1, **config["society"])
mc = MCMC(system=S, **config["mcmc"])
r = mc.sample()
# print("accptance ratio at {} was {}".format(
# (tau,beta,delta),
# (1 - mc.rejected/(S.N*mc.max_sweeps))
# ))
return idx, (tau, delta, beta), r
# result = map(run,args[:3])
pool = mp.Pool()
t0 = time.time()
print(40 * "=")
print("Passed:", json.dumps(config, indent=4), sep="\n")
print("Starting at: ", time.asctime())
result = pool.map(run, args)
pool.close()
pool.join()
result.sort()
t = time.time()
print("pool.map(run, args) took %s" % (timedelta(seconds=(t - t0))))
t0_save = time.time()
save_data(result, config)
t_save = time.time()
print("save_data took %s" % (timedelta(seconds=(t_save - t0_save))))
t_final = time.time()
print("Total time spent: %s" % (timedelta(seconds=(t_save - t0))))
print("Finished at: ", time.asctime())
print(40 * "=")
def save(self):
utility.save_data("postnr_addresses", self.places)
def save_urls(self):
utility.save_data("urls", self.url_lists)
def save_refs(self):
utility.save_data("spotify", self.references)
def solve_xor(learning_pulses,
epochs,
learn_hidden_synapses=False,
output_to_csv=False):
lrs_set_voltage = 1
hrs_set_voltage = -4
spike = 1
false_negative = 1
false_positive = -1
training_voltage = 1
input_spikes_1 = [0, 0, 1, 1]
input_spikes_2 = [0, 1, 0, 1]
targets = [0, 1, 1, 0]
# Create a network with 2 input neurons, 2 hidden layer neurons
# and 1 output neuron. The input neurons are simulated by their spikes.
hidden_neuron_1 = neuron.Neuron()
hidden_neuron_2 = neuron.Neuron()
output_neuron = neuron.Neuron()
# Create 6 synapses with input_synapse_ij representing the connection
# between the input neuron i and the hidden neuron j, and hidden_synapse_k
# representing the connection between the hidden neuron k and the output neuron.
# The inhibitory synapse is hidden_synapse_2.
input_synapse_11 = memristor.Memristor()
input_synapse_12 = memristor.Memristor()
input_synapse_21 = memristor.Memristor()
input_synapse_22 = memristor.Memristor()
hidden_synapse_1 = memristor.Memristor()
hidden_synapse_2 = memristor.Memristor()
# Set all the input synapses to a high resistance state.
input_synapse_11.set_resistance(hrs_set_voltage)
input_synapse_12.set_resistance(hrs_set_voltage)
input_synapse_21.set_resistance(hrs_set_voltage)
input_synapse_22.set_resistance(hrs_set_voltage)
# If the resistance values at the hidden synapses are going to be learned,
# then set all the hidden synapses to a high resistance state. Otherwise set
# all the hidden synapses to a low resistance state.
if learn_hidden_synapses:
hidden_synapse_1.set_resistance(hrs_set_voltage)
hidden_synapse_2.set_resistance(hrs_set_voltage)
else:
hidden_synapse_1.set_resistance(lrs_set_voltage)
hidden_synapse_2.set_resistance(lrs_set_voltage)
c_11 = []
c_12 = []
c_21 = []
c_22 = []
hidden_spikes_1 = []
hidden_spikes_2 = []
z_1 = []
z_2 = []
output_spikes = []
errors = []
squared_errors = []
epoch_numbers = []
for epoch_number in range(1, epochs + 1):
for spike_1, spike_2, target in zip(input_spikes_1, input_spikes_2,
targets):
epoch_numbers.append(epoch_number)
# The current c_ij passes through the input_synapse_ij.
# The input voltage is 1 V if the input neuron fired, else it is 0 V.
c_11.append(spike_1 / input_synapse_11.read_resistance())
c_12.append(spike_1 / input_synapse_12.read_resistance())
c_21.append(spike_2 / input_synapse_21.read_resistance())
c_22.append(spike_2 / input_synapse_22.read_resistance())
hidden_spikes_1.append(
hidden_neuron_1.apply_current(c_11[-1] + c_21[-1]))
hidden_spikes_2.append(
hidden_neuron_2.apply_current(c_12[-1] + c_22[-1]))
# The curent z_i passes through the hidden_synapse_i.
# The current in the inhibitory synapse is inverted.
# The input voltage is 1 V if the hidden neuron
# fired, else it is 0 V.
z_1.append(hidden_spikes_1[-1] /
hidden_synapse_1.read_resistance())
z_2.append(
utility.invert(hidden_spikes_2[-1] /
hidden_synapse_2.read_resistance()))
output_spikes.append(output_neuron.apply_current(z_1[-1] +
z_2[-1]))
errors.append(target - output_spikes[-1])
squared_errors.append(errors[-1]**2)
# Update the synapses based on the error.
# If the output neuron should have fired and it did not,
# then strengthen hidden synapse 1 if hidden neuron 1 fired
# and if the resistance values at the hidden synapses are to be learned.
# Otherwise strengthen the input synapses of the input neurons that fired
# and that are connected to hidden neuron 1.
if errors[-1] == false_negative:
if hidden_spikes_1[-1] == spike and learn_hidden_synapses:
for _ in range(learning_pulses):
hidden_synapse_1.apply_voltage(training_voltage)
else:
if spike_1 == spike:
for _ in range(learning_pulses):
input_synapse_11.apply_voltage(training_voltage)
if spike_2 == spike:
for _ in range(learning_pulses):
input_synapse_21.apply_voltage(training_voltage)
# If the output neuron should not have fired and it did,
# then do the equivalent update for hidden synapse 2 or
# for the input synapses that are connected to hidden neuron 2.
elif errors[-1] == false_positive:
if hidden_spikes_2[-1] == spike and learn_hidden_synapses:
for _ in range(learning_pulses):
hidden_synapse_2.apply_voltage(training_voltage)
else:
if spike_1 == spike:
for _ in range(learning_pulses):
input_synapse_12.apply_voltage(training_voltage)
if spike_2 == spike:
for _ in range(learning_pulses):
input_synapse_22.apply_voltage(training_voltage)
# Store the data into a data frame.
data = pd.DataFrame()
data["input_spike_1"] = input_spikes_1 * epochs
data["input_spike_2"] = input_spikes_2 * epochs
data["c_11"] = c_11
data["c_12"] = c_12
data["c_21"] = c_21
data["c_22"] = c_22
data["hidden_spike_1"] = hidden_spikes_1
data["hidden_spike_2"] = hidden_spikes_2
data["z_1"] = z_1
data["z_2"] = z_2
data["output_spike"] = output_spikes
data["target"] = targets * epochs
data["error"] = errors
data["squared_error"] = squared_errors
data["epoch"] = epoch_numbers
data["learning_pulses"] = [learning_pulses] * epochs * 4
data["training_voltage"] = [training_voltage] * epochs * 4
data["learn_hidden_synapses"] = [learn_hidden_synapses] * epochs * 4
if output_to_csv:
utility.save_data(data, "./output", "solve-xor")
# Plot the MSE as a function of epoch.
utility.plot_mse(data)
def save_urls(self):
utility.save_data("urls", self.url_list)
def save(self):
utility.save_data("places", self.places)
def save(self):
utility.save_data("favorites", self.favorites)
def save(self):
utility.save_data("reminders", self.reminders)
def save(self):
utility.save_data('schema_id', self.id_directory)
utility.save_data('schema_fav', self.id_presets)
def solve_xor_complex_noisy_snn(learning_pulses, epochs, output_to_csv=False):
hrs_set_voltage = -4
current_application_time = 500
spike = 1
training_voltage = 1
true = 20
false = 0
input_spikes_1 = [false, false, true, true]
input_spikes_2 = [false, true, false, true]
targets = [false, true, true, false]
# Create a network with 2 input neurons, 2 hidden layer neurons
# and 1 output neuron. The input neurons are simulated by their spikes.
hidden_neuron_1 = spiking_neuron.SpikingNeuron()
hidden_neuron_2 = spiking_neuron.SpikingNeuron()
output_neuron = spiking_neuron.SpikingNeuron()
# Create 6 synapses with pos_input_synapse_ij representing the excitatory
# connection between the input neuron i and the hidden neuron j, and
# pos_hidden_synapse_k representing the excitatory connection between
# the hidden neuron k and the output neuron.
pos_input_synapse_11 = memristor.Memristor()
pos_input_synapse_12 = memristor.Memristor()
pos_input_synapse_21 = memristor.Memristor()
pos_input_synapse_22 = memristor.Memristor()
pos_hidden_synapse_1 = memristor.Memristor()
pos_hidden_synapse_2 = memristor.Memristor()
# Create 6 synapses with neg_input_synapse_ij representing the inhibitory
# connection between the input neuron i and the hidden neuron j, and
# neg_hidden_synapse_k representing the inhibitory connection between
# the hidden neuron k and the output neuron.
neg_input_synapse_11 = memristor.Memristor()
neg_input_synapse_12 = memristor.Memristor()
neg_input_synapse_21 = memristor.Memristor()
neg_input_synapse_22 = memristor.Memristor()
neg_hidden_synapse_1 = memristor.Memristor()
neg_hidden_synapse_2 = memristor.Memristor()
# Set all the synapses to a high resistance state.
pos_input_synapse_11.set_resistance(hrs_set_voltage)
pos_input_synapse_12.set_resistance(hrs_set_voltage)
pos_input_synapse_21.set_resistance(hrs_set_voltage)
pos_input_synapse_22.set_resistance(hrs_set_voltage)
pos_hidden_synapse_1.set_resistance(hrs_set_voltage)
pos_hidden_synapse_2.set_resistance(hrs_set_voltage)
neg_input_synapse_11.set_resistance(hrs_set_voltage)
neg_input_synapse_12.set_resistance(hrs_set_voltage)
neg_input_synapse_21.set_resistance(hrs_set_voltage)
neg_input_synapse_22.set_resistance(hrs_set_voltage)
neg_hidden_synapse_1.set_resistance(hrs_set_voltage)
neg_hidden_synapse_2.set_resistance(hrs_set_voltage)
c_11 = []
c_12 = []
c_21 = []
c_22 = []
hidden_spikes_1 = []
hidden_spikes_2 = []
z_1 = []
z_2 = []
output_spikes = []
errors = []
squared_errors = []
epoch_numbers = []
for epoch_number in range(1, epochs + 1):
for spikes_1, spikes_2, target in zip(input_spikes_1, input_spikes_2, targets):
epoch_numbers.append(epoch_number)
# The normalized noisy current c_ij passes through the input_synapse_ij. The input
# voltage is 1 V if the input neuron fired as many pulses as the encoding of
# TRUE or more, else it is 0 V.
pos_c_11 = utility.add_noise(utility.normalize(utility.burst(spikes_1, true)
/ pos_input_synapse_11.read_resistance()))
pos_c_12 = utility.add_noise(utility.normalize(utility.burst(spikes_1, true)
/ pos_input_synapse_12.read_resistance()))
pos_c_21 = utility.add_noise(utility.normalize(utility.burst(spikes_2, true)
/ pos_input_synapse_21.read_resistance()))
pos_c_22 = utility.add_noise(utility.normalize(utility.burst(spikes_2, true)
/ pos_input_synapse_22.read_resistance()))
# The normalized noisy current neg_c_ij passes through the neg_input_synapse_ij.
# The current is inverted to represent the inhibitory connection. The input
# voltage is 1 V if the input neuron fired as many pulses as the encoding
# of TRUE or more, else it is 0 V.
neg_c_11 = utility.invert(utility.add_noise(utility.normalize(utility.burst(spikes_1, true)
/ neg_input_synapse_11.read_resistance())))
neg_c_12 = utility.invert(utility.add_noise(utility.normalize(utility.burst(spikes_1, true)
/ neg_input_synapse_12.read_resistance())))
neg_c_21 = utility.invert(utility.add_noise(utility.normalize(utility.burst(spikes_2, true)
/ neg_input_synapse_21.read_resistance())))
neg_c_22 = utility.invert(utility.add_noise(utility.normalize(utility.burst(spikes_2, true)
/ neg_input_synapse_22.read_resistance())))
c_11.append(pos_c_11 + neg_c_11)
c_12.append(pos_c_12 + neg_c_12)
c_21.append(pos_c_21 + neg_c_21)
c_22.append(pos_c_22 + neg_c_22)
hidden_spikes_1.append(hidden_neuron_1.apply_current(c_11[-1] + c_21[-1],
current_application_time)[0].count(spike))
hidden_spikes_2.append(hidden_neuron_2.apply_current(c_12[-1] + c_22[-1],
current_application_time)[0].count(spike))
# The normalized noisy curent pos_z_i passes through the pos_hidden_synapse_i.
# The input voltage is 1 V if the hidden neuron fired as many pulses as
# the encoding of TRUE or more, else it is 0 V.
pos_z_1 = utility.add_noise(utility.normalize(utility.burst(hidden_spikes_1[-1], true)
/ pos_hidden_synapse_1.read_resistance()))
pos_z_2 = utility.add_noise(utility.normalize(utility.burst(hidden_spikes_2[-1], true)
/ pos_hidden_synapse_2.read_resistance()))
# The normalized noisy curent neg_z_i passes through the neg_hidden_synapse_i.
# The current is inverted to represent the inhibitory connection. The
# input voltage is 1 V if the hidden neuron fired as many pulses as the
# encoding of TRUE or more, else it is 0 V.
neg_z_1 = utility.invert(utility.add_noise(utility.normalize(utility.burst(hidden_spikes_1[-1], true)
/ neg_hidden_synapse_1.read_resistance())))
neg_z_2 = utility.invert(utility.add_noise(utility.normalize(utility.burst(hidden_spikes_2[-1], true)
/ neg_hidden_synapse_2.read_resistance())))
z_1.append(pos_z_1 + neg_z_1)
z_2.append(pos_z_2 + neg_z_2)
output_spikes.append(output_neuron.apply_current(z_1[-1] + z_2[-1],
current_application_time)[0].count(spike))
errors.append(target - output_spikes[-1])
squared_errors.append(errors[-1] ** 2)
# Update the synapses based on the error.
# If the output neuron should have fired more times and it did not,
# then strengthen the excitatory hidden synapses of the hidden neurons
# that fired as many pulses as the encoding of TRUE or more. Otherwise,
# do the equivalent update for the input synapses of the input neurons
# that fired as many pulses as the encoding of TRUE or more.
if utility.is_false_negative(errors[-1]):
if utility.burst(hidden_spikes_1[-1], true) or utility.burst(hidden_spikes_2[-1], true):
if utility.burst(hidden_spikes_1[-1], true):
for _ in range(learning_pulses):
pos_hidden_synapse_1.apply_voltage(training_voltage)
if utility.burst(hidden_spikes_2[-1], true):
for _ in range(learning_pulses):
pos_hidden_synapse_2.apply_voltage(training_voltage)
else:
if utility.burst(spikes_1, true):
for _ in range(learning_pulses):
pos_input_synapse_11.apply_voltage(training_voltage)
pos_input_synapse_12.apply_voltage(training_voltage)
if utility.burst(spikes_2, true):
for _ in range(learning_pulses):
pos_input_synapse_21.apply_voltage(training_voltage)
pos_input_synapse_22.apply_voltage(training_voltage)
# If the output neuron should not have fired and it did, then strengthen
# the inhibitory hidden synapses of the hidden neurons that fired as many
# pulses as the encoding of TRUE or more.
elif utility.is_false_positive(errors[-1]):
if utility.burst(hidden_spikes_1[-1], true):
for _ in range(learning_pulses):
neg_hidden_synapse_1.apply_voltage(training_voltage)
if utility.burst(hidden_spikes_2[-1], true):
for _ in range(learning_pulses):
neg_hidden_synapse_2.apply_voltage(training_voltage)
# The neurons are resting until the next input.
hidden_neuron_1.rest()
hidden_neuron_2.rest()
output_neuron.rest()
# Store the data into a data frame.
data = pd.DataFrame()
data["input_spikes_1"] = input_spikes_1 * epochs
data["input_spikes_2"] = input_spikes_2 * epochs
data["c_11"] = c_11
data["c_12"] = c_12
data["c_21"] = c_21
data["c_22"] = c_22
data["hidden_spikes_1"] = hidden_spikes_1
data["hidden_spikes_2"] = hidden_spikes_2
data["z_1"] = z_1
data["z_2"] = z_2
data["output_spikes"] = output_spikes
data["target"] = targets * epochs
data["error"] = errors
data["squared_error"] = squared_errors
data["epoch"] = epoch_numbers
data["learning_pulses"] = [learning_pulses] * epochs * 4
data["training_voltage"] = [training_voltage] * epochs * 4
data["current_application_time"] = [current_application_time] * epochs * 4
if output_to_csv:
utility.save_data(data, "./output", "solve-xor-complex-noisy-snn")
# Plot the MSE as a function of epoch.
utility.plot_mse(data)
def mask_save(self):
utility.save_data("urlmasks", self.url_masks)
def save(self):
self.savable_environment.parent = None
utility.save_data("lisp_state", self.savable_environment)
self.savable_environment.parent = self.globals
def solve_xor_complex(learning_pulses, epochs, output_to_csv=False):
hrs_set_voltage = -4
spike = 1
false_negative = 1
false_positive = -1
training_voltage = 1
input_spikes_1 = [0, 0, 1, 1]
input_spikes_2 = [0, 1, 0, 1]
targets = [0, 1, 1, 0]
# Create a network with 2 input neurons, 3 hidden layer neurons
# and 1 output neuron. The input neurons are simulated by their spikes.
hidden_neuron_1 = neuron.Neuron()
hidden_neuron_2 = neuron.Neuron()
hidden_neuron_3 = neuron.Neuron()
output_neuron = neuron.Neuron()
# Create 7 synapses with pos_input_synapse_ij representing the excitatory
# connection between the input neuron i and the hidden neuron j, and
# pos_hidden_synapse_k representing the excitatory connection between
# the hidden neuron k and the output neuron.
pos_input_synapse_11 = memristor.Memristor()
pos_input_synapse_13 = memristor.Memristor()
pos_input_synapse_22 = memristor.Memristor()
pos_input_synapse_23 = memristor.Memristor()
pos_hidden_synapse_1 = memristor.Memristor()
pos_hidden_synapse_2 = memristor.Memristor()
pos_hidden_synapse_3 = memristor.Memristor()
# Create 7 synapses with neg_input_synapse_ij representing the inhibitory
# connection between the input neuron i and the hidden neuron j, and
# neg_hidden_synapse_k representing the inhibitory connection between
# the hidden neuron k and the output neuron.
neg_input_synapse_11 = memristor.Memristor()
neg_input_synapse_13 = memristor.Memristor()
neg_input_synapse_22 = memristor.Memristor()
neg_input_synapse_23 = memristor.Memristor()
neg_hidden_synapse_1 = memristor.Memristor()
neg_hidden_synapse_2 = memristor.Memristor()
neg_hidden_synapse_3 = memristor.Memristor()
# Set all the synapses to a high resistance state.
pos_input_synapse_11.set_resistance(hrs_set_voltage)
pos_input_synapse_13.set_resistance(hrs_set_voltage)
pos_input_synapse_22.set_resistance(hrs_set_voltage)
pos_input_synapse_23.set_resistance(hrs_set_voltage)
pos_hidden_synapse_1.set_resistance(hrs_set_voltage)
pos_hidden_synapse_2.set_resistance(hrs_set_voltage)
pos_hidden_synapse_3.set_resistance(hrs_set_voltage)
neg_input_synapse_11.set_resistance(hrs_set_voltage)
neg_input_synapse_13.set_resistance(hrs_set_voltage)
neg_input_synapse_22.set_resistance(hrs_set_voltage)
neg_input_synapse_23.set_resistance(hrs_set_voltage)
neg_hidden_synapse_1.set_resistance(hrs_set_voltage)
neg_hidden_synapse_2.set_resistance(hrs_set_voltage)
neg_hidden_synapse_3.set_resistance(hrs_set_voltage)
c_11 = []
c_13 = []
c_22 = []
c_23 = []
hidden_spikes_1 = []
hidden_spikes_2 = []
hidden_spikes_3 = []
z_1 = []
z_2 = []
z_3 = []
output_spikes = []
errors = []
squared_errors = []
epoch_numbers = []
for epoch_number in range(1, epochs + 1):
for spike_1, spike_2, target in zip(input_spikes_1, input_spikes_2,
targets):
epoch_numbers.append(epoch_number)
# The current pos_c_ij passes through the pos_input_synapse_ij.
# The input voltage is 1 V if the input neuron fired, else it is 0 V.
pos_c_11 = spike_1 / pos_input_synapse_11.read_resistance()
pos_c_13 = spike_1 / pos_input_synapse_13.read_resistance()
pos_c_22 = spike_2 / pos_input_synapse_22.read_resistance()
pos_c_23 = spike_2 / pos_input_synapse_23.read_resistance()
# The current neg_c_ij passes through the neg_input_synapse_ij.
# The current is inverted to represent the inhibitory connection.
# The input voltage is 1 V if the input neuron fired, else it is 0 V.
neg_c_11 = utility.invert(spike_1 /
neg_input_synapse_11.read_resistance())
neg_c_13 = utility.invert(spike_1 /
neg_input_synapse_13.read_resistance())
neg_c_22 = utility.invert(spike_2 /
neg_input_synapse_22.read_resistance())
neg_c_23 = utility.invert(spike_2 /
neg_input_synapse_23.read_resistance())
c_11.append(pos_c_11 + neg_c_11)
c_13.append(pos_c_13 + neg_c_13)
c_22.append(pos_c_22 + neg_c_22)
c_23.append(pos_c_23 + neg_c_23)
hidden_spikes_1.append(hidden_neuron_1.apply_current(c_11[-1]))
hidden_spikes_2.append(hidden_neuron_2.apply_current(c_22[-1]))
hidden_spikes_3.append(
hidden_neuron_3.apply_current(c_13[-1] + c_23[-1]))
# The curent pos_z_i passes through the pos_hidden_synapse_i.
# The input voltage is 1 V if the hidden neuron fired, else it is 0 V.
pos_z_1 = hidden_spikes_1[
-1] / pos_hidden_synapse_1.read_resistance()
pos_z_2 = hidden_spikes_2[
-1] / pos_hidden_synapse_2.read_resistance()
pos_z_3 = hidden_spikes_3[
-1] / pos_hidden_synapse_3.read_resistance()
# The curent neg_z_i passes through the neg_hidden_synapse_i.
# The current is inverted to represent the inhibitory connection.
# The input voltage is 1 V if the hidden neuron fired, else it is 0 V.
neg_z_1 = utility.invert(hidden_spikes_1[-1] /
neg_hidden_synapse_1.read_resistance())
neg_z_2 = utility.invert(hidden_spikes_2[-1] /
neg_hidden_synapse_2.read_resistance())
neg_z_3 = utility.invert(hidden_spikes_3[-1] /
neg_hidden_synapse_3.read_resistance())
z_1.append(pos_z_1 + neg_z_1)
z_2.append(pos_z_2 + neg_z_2)
z_3.append(pos_z_3 + neg_z_3)
output_spikes.append(
output_neuron.apply_current(z_1[-1] + z_2[-1] + z_3[-1]))
errors.append(target - output_spikes[-1])
squared_errors.append(errors[-1]**2)
# Update the synapses based on the error.
# If the output neuron should have fired and it did not,
# then strengthen the excitatory hidden synapses of the
# hidden neurons that fired. Otherwise, do the equivalent
# update for the input synapses of the input neurons that fired.
if errors[-1] == false_negative:
if hidden_spikes_1[-1] == spike or hidden_spikes_2[
-1] == spike or hidden_spikes_3[-1] == spike:
if hidden_spikes_1[-1] == spike:
for _ in range(learning_pulses):
pos_hidden_synapse_1.apply_voltage(
training_voltage)
if hidden_spikes_2[-1] == spike:
for _ in range(learning_pulses):
pos_hidden_synapse_2.apply_voltage(
training_voltage)
if hidden_spikes_3[-1] == spike:
for _ in range(learning_pulses):
pos_hidden_synapse_3.apply_voltage(
training_voltage)
else:
if spike_1 == spike:
for _ in range(learning_pulses):
pos_input_synapse_11.apply_voltage(
training_voltage)
pos_input_synapse_13.apply_voltage(
training_voltage)
if spike_2 == spike:
for _ in range(learning_pulses):
pos_input_synapse_22.apply_voltage(
training_voltage)
pos_input_synapse_23.apply_voltage(
training_voltage)
# If the output neuron should not have fired and it did, then
# strengthen the inhibitory hidden synapses of the hidden neurons
# that fired.
elif errors[-1] == false_positive:
if hidden_spikes_1[-1] == spike:
for _ in range(learning_pulses):
neg_hidden_synapse_1.apply_voltage(training_voltage)
if hidden_spikes_2[-1] == spike:
for _ in range(learning_pulses):
neg_hidden_synapse_2.apply_voltage(training_voltage)
if hidden_spikes_3[-1] == spike:
for _ in range(learning_pulses):
neg_hidden_synapse_3.apply_voltage(training_voltage)
# Store the data into a data frame.
data = pd.DataFrame()
data["input_spike_1"] = input_spikes_1 * epochs
data["input_spike_2"] = input_spikes_2 * epochs
data["c_11"] = c_11
data["c_13"] = c_13
data["c_22"] = c_22
data["c_23"] = c_23
data["hidden_spike_1"] = hidden_spikes_1
data["hidden_spike_2"] = hidden_spikes_2
data["hidden_spike_3"] = hidden_spikes_3
data["z_1"] = z_1
data["z_2"] = z_2
data["z_3"] = z_3
data["output_spike"] = output_spikes
data["target"] = targets * epochs
data["error"] = errors
data["squared_error"] = squared_errors
data["epoch"] = epoch_numbers
data["learning_pulses"] = [learning_pulses] * epochs * 4
data["training_voltage"] = [training_voltage] * epochs * 4
if output_to_csv:
utility.save_data(data, "./output", "solve-xor-complex")
# Plot the MSE as a function of epoch.
utility.plot_mse(data)
def save(self):
utility.save_data("reminders", self.reminders)
def save(self):
utility.save_data("insults", self.insults)
def save(self):
utility.save_data("compliments", self.compliments)
def save(self):
utility.save_data("rss_watch_list", self.watch_list)
def set_notes(self, nick, text):
self.notebook[nick] = text
utility.save_data('notes', self.notebook)
def save(self):
utility.save_data("stockaliases", self.__aliases)
def save(self):
utility.save_data("postnr_addresses", self.places)
def set_location(self, value):
self.location = value
utility.save_data('festern_bbq', self.location)
def save(self):
utility.save_data('schema_id', self.id_directory)
utility.save_data('schema_fav', self.id_presets)
def save_refs(self):
utility.save_data("spotify", self.references)
# S = Society(delta=delta, beta=beta, tau=0.1, **config["society"])
mc = MCMC(system=S, **config["mcmc"])
r = mc.sample()
# print("accptance ratio at {} was {}".format(
# (tau,beta,delta),
# (1 - mc.rejected/(S.N*mc.max_sweeps))
# ))
return idx, (tau, delta, beta), r
# result = map(run,args[:3])
pool = mp.Pool()
t0 = time.time()
print(40*"=")
print("Passed:", json.dumps(config, indent=4), sep="\n")
print("Starting at: ", time.asctime())
result = pool.map(run, args)
pool.close()
pool.join()
result.sort()
t = time.time()
print("pool.map(run, args) took %s"%(timedelta(seconds=(t-t0))))
t0_save = time.time()
save_data(result, config)
t_save = time.time()
print("save_data took %s"%(timedelta(seconds=(t_save-t0_save))))
t_final = time.time()
print("Total time spent: %s"%(timedelta(seconds=(t_save-t0))))
print("Finished at: ", time.asctime())
print(40*"=")
