Python 2.7-将自定义对象添加到列表，然后打印出属性会打印随机值

Question

我有这个自定义对象：

class Neuron(object):
    def __init__(self):
            self.bias = 1.0
        self.weightBias = uniform(-1, 1)
        # self.weightBias = 1000
        self.area = 0.0
        self.weightArea = uniform(-1, 1)
        # self.weightArea = 1000
        self.perimeter = 0.0
        self.weightPerimeter = uniform(-1, 1)
        # self.weightPerimeter = 1000
        self.compactness = 0.0
        self.weightCompactness = uniform(-1, 1)
        # self.weightCompactness = 1000
        self.length = 0.0
        self.weightLength = uniform(-1, 1)
        # self.weightLength = 1000
        self.width = 0.0
        self.weightWidth = uniform(-1, 1)
        # self.weightWidth = 1000
        self.asymmetryCoefficient = 0.0
        self.weightAsymmetryCoefficient = uniform(-1, 1)
        # self.weightAsymmetryCoefficient = 1000
        self.lengthGroove = 0.0
        self.weightLengthGroove = uniform(-1, 1)
        # self.weightLengthGroove = 1000
        self.activationValue = 0.0
        self.output = 0

我有条件，在其中有一个我的Neuron类的实例添加到名为errorNeuron1的列表中：

# Calculates the output, 0 or 1, for the neuron
def calculateOutput(neuron):
    activationValue = neuron.bias * neuron.weightBias
    activationValue += neuron.area * neuron.weightArea
    activationValue += neuron.perimeter * neuron.weightPerimeter
    activationValue += neuron.compactness * neuron.weightCompactness
    activationValue += neuron.length * neuron.weightLength
    activationValue += neuron.width * neuron.weightWidth
    activationValue += neuron.asymmetryCoefficient * neuron.weightAsymmetryCoefficient
    activationValue += neuron.lengthGroove * neuron.weightLengthGroove

    neuron.activationValue = activationValue
    neuron.output = 1 if activationValue >= 0 else 0
    return neuron


# Retrieves data from row
def parseRow(neuron, row):
    neuron.area = float(row[1][0])
    neuron.perimeter = float(row[1][1])
    neuron.compactness = float(row[1][2])
    neuron.length = float(row[1][3])
    neuron.width = float(row[1][4])
    neuron.asymmetryCoefficient = float(row[1][5])
    neuron.lengthGroove = float(row[1][6])

    return neuron

def main():
    df = importCSV('trainSeeds.csv')

    neuron1 = Neuron()
    neuron2 = Neuron()

    errorNeuron1 = []
    errorNeuron2 = []
    for i in range(0, 1):
        # Iterate over dataframe rows
        for row in df.iterrows():
            neuron1 = parseRow(neuron1, row)
            # print(neuron1.area)
            neuron1 = calculateOutput(neuron1)

            expectedOutput = row[1][7]

            neuron2 = parseRow(neuron2, row)
            neuron2 = calculateOutput(neuron2)

            expectedOutputBinary = format(int(expectedOutput), '02b')

            # If the expectedOutput is equal to the output of the first node
            if int(expectedOutputBinary[0]) != neuron1.output:
                print(neuron1.activationValue)
                errorNeuron1.append((neuron1.activationValue, row))

                # neuron1 = calculateNewWeights(neuron1.output, neuron1, int(expectedOutputBinary[0]))
            elif int(expectedOutputBinary[1]) != neuron2.output:
                errorNeuron2.append(neuron2)
                # neuron2 = calculateNewWeights(neuron1.output, neuron2, int(expectedOutputBinary[1]))

        # errorNeuron1.sort(key=lambda x: x.activationValue)

        print("----------------------------------------")

        for n in errorNeuron1:
            print n

如何将自定义对象添加到列表，然后在不获取垃圾重复值的情况下打印每个值？另外，在这种情况下到底发生了什么？

这是我在添加到errorNeuron之前打印时的输出：

  

1.37930801491   1.3248274347   1.38542318785   1.39868417375   1.32901788598   1.38486475771   1.42985927348   1.41234073472   1.39305049211   1.36992907231   1.44747542914   1.3691793289   1.44158908789   1.4216161728   1.40943076719   1.41935402214   1.47371279276   1.33364482064   1.32809690259   1.46776554321   1.42706344947   1.41345102369   1.30522785708   1.38857331887   1.35931718327   1.31726730896   1.43862228814   1.41687773781   1.4188485406   1.45373663027   1.36475688453   1.41244050385   1.47747389879   1.39913433929   1.39572965064   1.37398585466   1.39037534707   1.38703336165   1.43049919876   1.53328689772   1.42474806554   1.41506511897   1.40265486573   1.49820537833   1.42285883588   1.36153921531   1.34902938453   1.40761425156   1.39036430332   1.38797706537   1.45461203476   1.47017316218   1.45939109969   1.43000636609   1.38050268146

这是我遍历errorNeuron1中的每个项目时的输出

  

1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648   1.50138570648