Python RKF.func_to_calc_sigmoid 예제들

예제 #1

def get_out(out_state):
    arr = []

    for i in range(NUM_NEURONS):
        arr.append(int(round(rkf.func_to_calc_sigmoid(out_state[i][2]))))

    return arr

예제 #2

파일: chaotic_dyn_learning_NN.py 프로젝트: Polladin/TCNN

def get_out(out_state):
    arr = []

    for i in range(NUM_NEURONS):
        arr.append(int(round(rkf.func_to_calc_sigmoid(out_state[i][2]))))

    return arr

예제 #3

파일: chaotic_dyn_learning_NN.py 프로젝트: Polladin/TCNN

def calc_S(step, neuron):
    sum_S = 0

#     print("exp func : " +  str(intern_state[step][j][2]))

    for j in range(NUM_NEURONS):
            sum_S += W[neuron][j] * rkf.func_to_calc_sigmoid(intern_state[step][j][2])

#     print(sum_S)
    return sum_S

예제 #4

def calc_S(step, neuron):
    sum_S = 0

    #     print("exp func : " +  str(intern_state[step][j][2]))

    for j in range(NUM_NEURONS):
        sum_S += W[neuron][j] * rkf.func_to_calc_sigmoid(
            intern_state[step][j][2])


#     print(sum_S)
    return sum_S

예제 #5

def print_out(out_state):

    st_outr = ''
    idx = 0

    for i in range(NUM_NEURONS):
        st_outr += str(int(round(rkf.func_to_calc_sigmoid(out_state[i][2]))))
        idx += 1

        if idx == LINE_AMOUNT:
            print(st_outr)
            st_outr = ''
            idx = 0

    print(st_outr)

예제 #6

파일: chaotic_dyn_learning_NN.py 프로젝트: Polladin/TCNN

def print_out(out_state):

    st_outr = ''
    idx = 0

    for i in range(NUM_NEURONS):
        st_outr += str(int(round(rkf.func_to_calc_sigmoid(out_state[i][2]))))
        idx += 1

        if idx == LINE_AMOUNT:
            print(st_outr)
            st_outr=''
            idx = 0

    print(st_outr)