小编Pet*_*eng的帖子

我试图理解decision_function和predict之间的关系,它们是SVC的实例方法(http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html).到目前为止,我已经收集到决策函数返回类之间的成对分数.我的印象是预测选择最大化其成对分数的类,但我测试了它并得到了不同的结果.这是我用来尝试理解两者之间关系的代码.首先我生成了成对分数矩阵,然后我打印出了具有最大配对分数的类,该分数与clf.predict预测的类不同.

        result = clf.decision_function(vector)[0]
        counter = 0
        num_classes = len(clf.classes_)
        pairwise_scores = np.zeros((num_classes, num_classes))
        for r in xrange(num_classes):
            for j in xrange(r + 1, num_classes):
                pairwise_scores[r][j] = result[counter]
                pairwise_scores[j][r] = -result[counter]
                counter += 1

        index = np.argmax(pairwise_scores)
        class = index_star / num_classes
        print class
        print clf.predict(vector)[0]

有谁知道这些预测和决策功能之间的关系？

我正在尝试创建一个api,您可以使用django rest框架使用单个Post请求创建多个对象.但是,我的序列化程序提供了一个我不明白的错误.首先,这是我的代码的骨架

我的序列化器:

class MyModelSerializer(serializers.ModelSerializer):
# override the constructor to always use the many flag
def __init__(self, *args, **kwargs):
    many = kwargs.pop('many', True)
    super(MyModelSerializer, self).__init__(many=many, *args, **kwargs)

class Meta:
    model = MyModel
    fields = ('field1', 'field2')

我的列表视图

class MyModelListView(generics.ListCreateAPIView):
    queryset = MyModel.objects.all()
    serializer_class = MyModelSerializer

    def post(self, request, *args, **kwargs):
        if request.DATA['batch']:
            json = request.DATA['batchData']
            stream = StringIO(json)
            data = JSONParser().parse(stream)
            request._data = data
        return super(CharacterDatumList, self).post(request, *args, **kwargs)

我的ajax电话

$.ajax({
    type: "POST",
    url: '/characterDatum/',
    data: {
        'batch' : true, …

我一直试图在heroku上安装opencv无济于事.我尝试使用heroku-anvil进行安装,现在尝试通过bash shell安装opencv.首先我安装了cmake,但是当我尝试makeopencv时,我不可避免地得到以下错误:

Linking CXX shared library ../../lib/cv2.so
/usr/bin/ld: /usr/local/lib/libpython2.7.a(abstract.o): relocation R_X86_64_32 against `.rodata.str1.8' can not be used when making a shared object; recompile with -fPIC
/usr/local/lib/libpython2.7.a: could not read symbols: Bad value
collect2: ld returned 1 exit status
make[2]: *** [lib/cv2.so] Error 1
make[1]: *** [modules/python/CMakeFiles/opencv_python.dir/all] Error 2
make: *** [all] Error 2

以下是我正在使用的确切命令:

# connect to bash shell

heroku run bash
cd /tmp

# Downloading and Installing cmake:

curl -s http://www.cmake.org/files/v2.8/cmake-2.8.12.1.tar.Z > cmake.tar.gz
tar zxf cmake.tar.gz
cd …