Python 初始化权重后scikit学习分类器精度的降低

我想在sklearn分类器的基础上实现adaboost分类器，在alg分类器的第一步，我应该将权重初始化为“1/#训练数据” 但是它会降低分类器的准确度，我不知道为什么？（我为所有数据点设置相同的权重）

我的代码：

svm_weight = SVC()
svm_non_weight = SVC()

w = np.ones(len(target_train))
w.fill(float(1)/float(len(target_train)))
svm_weight.fit(data_train_feature_scaled_pca,
               target_train,
               sample_weight= w)

svm_non_weight.fit(data_train_feature_scaled_pca,
                   target_train)

print "score weight : ",svm_weight.score(data_test_feature_scaled_pca,target_train)
print "score non weight : ",svm_non_weight.score(data_test_feature_scaled_pca,target_train)

输出：

score weight :  0.503592561285
score non weight :  0.729289940828

已实施的adaboost：

class adaboost_classifier:
    def __init__(self,train,target,classifier,n_estimator):
        #prepairing dataset
        self.N_classes = np.unique(target)
        self.n_estimator = n_estimator
        self.N_data = len(train)
        self.trained_classifier = [[classifier,float(0),float(0), True ] for i in range(n_estimator)]
        indice = []
        train = np.array(train)
        target = np.array(target)
        dataset = np.concatenate((train,target),axis=1)
        #join train and target for boosting

        for i in range(len(dataset[0])-1):
            indice.append(i)

        self.weights = np.zeros([n_estimator,self.N_data])

        # init 1/n value for weights
        self.weights.fill(1/float(self.N_data))
        #take sampling
        new_dataset = dataset
        self.N_data = len(new_dataset)
        #start training sub classifier
        for i in range(n_estimator):
            self.loss = np.zeros(self.N_data)
            #seprating training and target data
            new_train = new_dataset[:,indice]
            new_target = new_dataset[:,(len(dataset[0])-1)]
            #train the classifier : learn f(X) with data weight
            self.trained_classifier[i][0].fit(new_train,new_target,sample_weight=self.weights[i])
            #computed the weighted error wich is store in trained_classifier[i][1]
            for point in range(self.N_data) :
                if(self.trained_classifier[i][0].predict([new_train[point]]) != new_target[point]):
                    self.loss[point] = 1
                    self.trained_classifier[i][1] += self.weights[i][point]

            #compute coefficient of classifier i wich is store in trained_classifier[i][2]
            self.trained_classifier[i][2] = 0.5 * np.log((1-self.trained_classifier[i][1])/self.trained_classifier[i][1])
            #recompute weights
            for j in range(self.N_data):
                if(self.loss[j] == 1):
                    self.weights[i][j] *= np.exp(self.trained_classifier[i][2])
                else:
                    self.weights[i][j] *= np.exp(-self.trained_classifier[i][2])

            #normalizing the weights
            self.trained_classifier[i][1] = self.trained_classifier[i][1] / self.weights[i].sum()