all_three_bit_recursive_functions.py

import os
import sys
import time
import numpy as np
import pandas as pd
import booleanNetwork as bn
from reservoir import Reservoir
from output_layer import OutputLayer
import function_arguments as f_utils

start = time.clock()
N_list = [100] # [10, 20, 30, 40, 50, 100, 200, 300, 400, 500]
K = 2.0
I = 1
L_list = [50]  # [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
window = 3
delay  = 3
dataStreamLength = 10
trainingSize = 150
testSize = 150
O = 2 ** (2 ** window)
seed = 123 #int(sys.argv[1])

np.random.seed(seed)

# Initialize functions and inputs
#functionInputs = [f_utils.getInputTuple(window) for _ in range(O)]
#functionInputs = [[(0, 0), (0, 1), (-1, 1)]]
functionInputs = [ [(0, 0), (0, 1), (-1, 1)] for _ in range(O)]
recursiveArgs  = [ [(0,2)]  for _ in range(O) ]

functionsToApproximate = []
for i in range(O):
    function_vector = f_utils.convertIntToBinaryVector(i, 2 ** window)
    func = f_utils.convertVectorToFunction(function_vector)
    functionsToApproximate.append(func)

# Test parameter set
data = np.zeros((len(N_list), len(L_list)))

for i in range(len(N_list)):
    for j in range(len(L_list)):
        L = L_list[j] * N_list[i] // 100

        # Initialize reservoir
        bn_directory = os.getcwd() + '/../BN_realization/'
        directory = os.getcwd() + '/../output/'
        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 + 'recursive_funs_'+str(seed)+'.csv', N_list[i], varF, F, init)

        # Train and test output layer
        output = OutputLayer(res, O, functionsToApproximate, functionInputs,
                             delay, dataStreamLength,
                             nonRecursiveArgs=recursiveArgs)
        output.train(trainingSize)
        output.test(testSize)

        # add results to data
        data[i, j] = sum([output.successRates[k] for k in range(O)]) / O

        #for k in range(O) :
        #    print("{0:d},{1:.3f},{2:d},{3:d},{4:d},{5:.5f}".format( seed,K,N_list[i],L_list[j],k,output.successRates[k] ))

time = int(time.clock() - start)
# Write metadata to file
f_utils.printParameters(N_list, K, I, L_list, window, delay, dataStreamLength,
                        trainingSize, testSize, O, 'allThreeBitRecursive', seed, time)

# write data to file
df = pd.DataFrame(data)
df.index = N_list
print(df.to_csv(header=L_list))