qq_1277054161-优快云博客

原创 2020-09-01

LRU class DLinkedNode: def __init__(self, key=0, value=0): self.key = key self.value = value self.prev = None self.next = None class LRUCache: def __init__(self, capacity: int): self.cache = dict()

2020-09-01 00:13:22 162

剑指 Offer 12. 矩阵中的路径 python 请设计一个函数，用来判断在一个矩阵中是否存在一条包含某字符串所有字符的路径。路径可以从矩阵中的任意一格开始，每一步可以在矩阵中向左、右、上、下移动一格。如果一条路径经过了矩阵的某一格，那么该路径不能再次进入该格子。例如，在下面的3×4的矩阵中包含一条字符串“bfce”的路径（路径中的字母用加粗标出）。 [[“a”,“b”,“c”,“e”], [“s”,“f”,“c”,“s”], [“a”,“d”,“e”,“e”]] 但矩阵中不包含字符串“abfb”的路径

2020-06-26 23:04:36 455

原创旋转数组的最小数字 python

旋转数组的最小数字 python 旋转数组的最小数字 python class Solution(object): def minArray(self, numbers): """ :type numbers: List[int] :rtype: int """ if not numbers: return None def MinInOrder(numbers,

2020-06-26 21:37:30 284

翻译 Affordance Detection of Tool Parts from Geometric Features

@ICRA 2015 背景介绍 Affordence 解释：Affordence 本文提出从定位和几何原语提出两种方法学习Affordence：基于高像素的层次匹配（S-HMP）和结构化随机森林（SRF）。 S-HMP 深度特征：首先应用平滑和插值算子来减少噪声和失去的深度值。然后，从块中减去平均值获得深度绝对变化的鲁帮性。这些块直接用HMP学习层次稀疏编码字典。第一层，HMP捕捉原始结构像变化方...

2019-11-18 21:29:36 547

翻译 RGB-D物体检测无监督特征学习

@Unsupervised Feature Learning for RGB-D Based Object Recognition RGB-D物体检测无监督特征学习这篇论文主要提出层次匹配追踪（HMP）, HMP 以无监督方式对原RGB-D数据使用稀疏编码学习层次特征表示。模型 HMP使用彩色图像和深度图像。彩色图像对于外观细节很重要的对象实例识别非常有用，而RGB-D中的深度信息可以极大...

2019-11-18 11:13:22 683

模式识别中科院刘成林作业一

模式识别第一章作业题，中科院刘成林，Question 1 (Pattern Classiﬁcation, Chapter 2, Problem 12) Let ωmax(x) be the state of nature for which P(ωmax|x) ≥ P(ωi|x) for all i, i = 1,...,c. (a) Show that P(ωmax|x) ≥ 1/c (b) Show that for the minimum-error-rate decision rule the average probability of error is given by P(error) = 1−RP(ωmax|x)p(x)dx (c) Use these two results to show that P(error) ≤ (c−1)/c (d) Describe a situation for which P(error) = (c−1)/c Question 2 (Pattern Classiﬁcation, Chapter 2, Problem 13) In many pattern classiﬁcation problems one has the option either to assign the pattern to one of c classes, or to reject it as being unrecognizable. If the cost for rejects is not too high, rejection may be a desirable action. Let λ(αi|ωi) =     0 i = j i,j = 1,...,c λr i = c + 1 λs otherwise where λr is the loss incurred for choosing the (c + 1)th action, rejection, and λs is the loss incurred for making a substitution error. Show that the minimum risk is obtained if we decide ωi if P(ωi|x) ≥ P(ωi|x) for all j and if P(ωi|x) ≥ 1− λr λs , and reject otherwise. What happens if λr = 0? What happens if λr > λs? Question 3 Now we have N samples, and each sample xi, i = 1,...,N has d-dimensions. Please provide us the proofs and the pseudo-codes of PCA algorithm Question 4 (Pattern Classiﬁcation, Chapter 2, Problem 10) Consider the following decision rule for a two-category one-dimensional problem: Decide ω1 if x > θ; otherwise decide ω2. (a)Showtheprobabilityoferrorforthisruleisgivenby P(error) = P(ω1)Rθ−∞p(x|ω1)dx+P(ω2)R∞ θ p(x|ω2)dx (b) By diﬀerentiating, show that a necessary condition to minimize P(error) is that θ satisfy p(θ|ω1)P(ω1) = p(θ|ω2)P(ω2) (c) Does this equation deﬁne θ uniquely? (d) Give an example where a value of θ satisfying the equation actually maximizes the probability of error. Question 5 (Pattern Classiﬁcation, Chapter 2, Problem 24) Consider the multivariate normal density for which σij = 0 and σii = σ2 i , i.e., Σ = diag(σ2 1,σ2 2,...,σ2 d). (a) Show that

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原创 2020-09-01

原创 剑指 Offer 12. 矩阵中的路径 python

原创 旋转数组的最小数字 python