print('reservoir initialized')
functionInputs = [f_utils.getInputTuple(window) for i in range(O)]
functionsToApproximate, functionVectors = [], []
for i in range(O):
function_vector = f_utils.convertIntToBinaryVector(i, 2**window)
func = f_utils.convertVectorToFunction(function_vector)
functionsToApproximate.append(func)
# Only for printing purposes
functionVectors.append(function_vector)
output = OutputLayer(res, O, functionsToApproximate, functionInputs, delay,
dataStreamLength)
output.train(trainingSize)
output.test(testSize)
print(f'N = {N}')
print(f'K = {K}')
print(f'I = {I}')
print(f'L = {output.reservoir.L}')
print(f'window = {window}')
print(f'delay = {delay}')
print(f'dataStreamLength = {dataStreamLength}')
print(f'trainingSize = {trainingSize}')
print(f'testSize = {testSize}')
print(f'seed = {seed}')
print(f'O = {O}')
print()
print('Function,Accuracy')
print('\n\n')
print('X_train =')
print(d)
print(len(d))
print(len(d[0]))
print('\n\n')
print(len(b))
print(len(b[0]))
print(len(b[0, 0]))
print(e)
print(len(e))
print(len(e[0]))
results = output.test(testSize)
y_tests, y_predicts = results
differences = []
for i in range(len(y_tests)):
difference = []
for j in range(len(y_tests[i])):
difference.append(abs(y_tests[i, j] - y_predicts[i, j]))
differences.append(difference)
success_rates = []
for i in range(len(y_tests)):
success_rates.append(1 - (sum(differences[i]) / len(y_tests[i])))
print(success_rates)
