我们可以先获取升序排列的索引列表,然后再将其倒序排列: descending_indices=np.argsort(arr)[::-1] 1. 这样我们就得到了按照值从大到小排列的索引列表descending_indices。 代码示例 下面是完整的代码示例: importnumpyasnp arr=np.array([3,1,4,1,5,9,2,6,5,3])descending_indi
SortedIndices(Descending):[524031]SortedElements(Descending):[1086532] 通过对NumPy数组取反排序 在这个例子中,我们对NumPy数组取反,使用numy.argsort()按降序对其进行排序。在这里,我们将使用argsort()来获取索引,这些索引将按升序对原始数组进行排序。现在,为了获得按降序排序数组的索引,我们首先使用-arr对数组求反...
>>> s = sorted(student_objects, key=attrgetter('age'))#sort on secondary key>>> sorted(s, key=attrgetter('grade'), reverse=True)#now sort on primary key, descending[('dave','B', 10), ('jane','B', 12), ('john','A', 15)] 6)numpy中数组矩阵的排序方法argsort() argsort(a, ...
pred = pred[pred[:, 4].argsort(descending=True)] # Batched NMS # Batched NMS推理时间:0.054 if method == 'hard_nms_batch': # 普通的(hard)nms: 官方实现(c函数库),可支持gpu,但支持多类别输入 # batched_nms:参数1 [43, xyxy] 参数2 [43, score] 参数3 [43, class] 参数4 [43, nms...
# Invert rows with [:, ::-1] to make sorting descending sorting = np.argsort(lda.components_, axis=1)[:, ::-1] # get the feature names from the vectorizer: feature_names = np.array(vect.get_feature_names()) # Print out the 10 topics: mglearn.tools.print_topics(topics=range(10...
idx = scores.argsort(descending=True) # descending rank by score bboxes = bboxes[idx] scores = scores[idx] if len(scores) > pre_num: bboxes = bboxes[:pre_num] scores = scores[:pre_num] # non-maximum suppression, we use cuda version of nms ...
argsort:它是沿指定轴的间接排序,本文不讲解。 msort:按值对 input 张量沿其第一维以升序排序。torch.msort(t) 等效于 torch.sort(t, dim=0)。 sort 可以降序或升序,参数说明如下: torch.sort(input, dim=-1, descending=False, stable=False, *, out=None) ...
descending argsort: [4 6 3 2 5 1 7 0]''' c++ #include <vector>#include<iostream>usingnamespacestd; template<typename T>vector<int> sort_indexes(constvector<T> & v,boolreverse=false) {//initialize original index locationsvector<int>idx(v.size());for(inti =0; i != idx.size(); ...
ReadUse np.argsort in Descending Order in Python Integer Division Be cautious with integer arrays – division in NumPy follows Python’s behavior: import numpy as np # Integer division (Python 3) int_array = np.array([5, 10, 15, 20]) ...
[ 2, 20, 300]]) >>> a[a[:,2].argsort()][::-1] #sort by the 3rd column descending array([[ 2, 20, 300], [ 1, 30, 200], [ 3, 10, 100]]) >>> a[a[:,1].argsort()] #sort by the 2nd column ascending array([[ 3, 10, 100], [ 2, 20, 300], [ 1, 30, ...