isConstantConnectivity=False)
res = Reservoir(I, L, bn_directory + 'time_series_data_3.csv',
directory + 'experiment1_2018-08-03.csv', N_list[i],
varF, F, init)
# Train and test output layer
output = OutputLayer(res,
O,
functionsToApproximate,
functionInputs,
delay,
dataStreamLength,
nonRecursiveArgs=[[(0, 2)]])
output.train(trainingSize)
output.test(testSize)
# add results to data
print('adding data')
data[i, j] = sum([output.successRates[k] for k in range(O)]) / O
time = int(time.clock() - start)
# Write metadata to file
f_utils.printParameters(N_list, K, I, L_list, window, delay, dataStreamLength,
trainingSize, testSize, O, 'recursiveParity', seed,
time)
# write data to file
df = pd.DataFrame(data)
df.index = N_list
print(df.to_csv(header=L_list))
L = L_list[j] * N_list[i] // 100
# Initialize reservoir
bn_directory = os.getcwd() + '/BN_realization/'
directory = os.getcwd() + '/'
varF, F, init = bn.getRandomParameters(N_list[i] + I,
K + (L / N_list[i]),
isConstantConnectivity=False)
res = Reservoir(I, L, bn_directory + 'time_series_data_3.csv',
directory + 'experiment1_2018-08-03.csv', N_list[i],
varF, F, init)
# Train and test output layer
output = OutputLayer(res, O, functionsToApproximate, functionInputs,
delay, dataStreamLength)
output.train(trainingSize)
output.test(testSize)
# add results to data
data[i, j] = sum([output.successRates[k] for k in range(O)]) / O
time = int(time.clock() - start)
# Write metadata to file
f_utils.printParameters(N_list, K, I, L_list, window, delay, dataStreamLength,
trainingSize, testSize, O, 'allThreeBit', seed, time)
# write data to file
df = pd.DataFrame(data)
df.index = N_list
print(df.to_csv(header=L_list))