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【CodeForces 621A】Wet Shark and Odd and Even

题 Today, Wet Shark is given n integers....Please, calculate this value for Wet Shark....Note, that if Wet Shark uses no integers from the n integers, the sum is an even integer 0....The next line contains n space separated integers given to Wet Shark....In the second sample Wet Shark should take any four out of five integers 999 999 999.

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【CodeForces 621B】Wet Shark and Bishops

1000*1000的格子里，给你n≤200 000个点的坐标，求有多少对在一个对角线上。

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【CodeForces 621C】Wet Shark and Flowers

题 There are n sharks who grow flowers for Wet Shark....Wet Shark has it's favourite prime number p, and he really likes it!...If for any pair of neighbouringsharks i and j the product si·sj is divisible by p, then Wet Shark becomes...At the end of the day sharks sum all the money Wet Shark granted to them....contains two space-separated integers n and p (3 ≤ n ≤ 100 000, 2 ≤ p ≤ 109) — the number of sharks and Wet

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【CodeForces】#621A - Wet Shark and Odd and Even（水）

Wet Shark and Odd and Even time limit per test 2 seconds memory limit per test...Please, calculate this value for Wet Shark....Note, that if Wet Shark uses no integers from the n integers, the sum is an even integer 0....The next line contains n space separated integers given to Wet Shark....In the second sample Wet Shark should take any four out of five integers 999 999 999.

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【CodeForces】#621B - Wet Shark and Bishops（思维）

Wet Shark and Bishops time limit per test 2 seconds memory limit per test 256...megabytes input standard input output standard output Today, Wet Shark is...Wet Shark thinks that two bishops attack each other if they share the same diagonal....Note, that this is the only criteria, so two bishops may attack each other (according to Wet Shark) even...Now Wet Shark wants to count the number of pairs of bishops that attack each other.

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关于马尔可夫链的一道题目

问题解答 python模拟问题某人有 2 把伞，并在办公室和家之间往返．如果某天他在家中(办公室时)下雨而且家中(办公室)有伞他就带一把伞去上班(回家)，不下雨时他从不带伞．如果每天与以往独立地早上...，转移概率为 (下雨从手边带一把伞走), (只是去了另一边，不带伞)，因此转移矩阵为：设平稳状态概率分别为根据转移矩阵容易求得淋雨的概率则为约等于 0.0913 python...+= 0 else: get_wet += 1 else: get_wet...+= 0 else: get_wet += 1 else: get_wet...+= 0 print(get_wet/2/n) 0.09133

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在没有数据的情况下使用贝叶斯定理设计知识驱动模型

所有示例都是使用 python 的 bnlearn 库创建的。我们能把专家的知识运用到模型中去吗? 当我们谈论知识时，它不仅仅是描述性的知识和事实。...因此对应的P(wet grass=0|rain=1，sprinkler =1)=1 - 0.99 = 0.01 作为专家我完全肯定，没有下雨或者没有开洒水器的时候草不会湿:P(wet grass=0 |...P(wet grass=1 | rain=1，sprinkler =0)= 0.9。对应的是:P(wet grass=0 | rain=1，sprinkler =0)=1 - 0.9 = 0.1。...P(wet grass=1 | rain=0，sprinkler =1)= 0.9。对应的是:P(wet grass=0 | rain=0，sprinkler =1)=1 - 0.9 = 0.1。...E.Taskesen, A Step-by-Step Guide in detecting causal relationships using Bayesian Structure Learning in Python

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（一）ROS系统入门 Getting Started with ROS 以Kinetic为主更新附课件PPT

--tar > indigo-desktop-full-wet.rosinstall $ wstool init -j8 src indigo-desktop-full-wet.rosinstall...--tar > indigo-desktop-wet.rosinstall $ wstool init -j8 src indigo-desktop-wet.rosinstall ROS-Comm:...$ wstool init -j8 src indigo-ros_comm-wet.rosinstall This will add all of the catkin or wet packages...--tar > indigo-robot-wet.rosinstall $ wstool init -j8 src indigo-robot-wet.rosinstall If wstool init...$ wstool init -j8 src indigo-desktop-wet.rosinstall This will add all of the catkin or wet packages

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【GEE】基于PCA的LANDSAT 8计算遥感生态指数（RSEI）

); return wet_temp; } function NDWI_cal(img) { var nir = img.select("SR_B5"); var green = img.select...dataset_no_water); var NDBSI =(SI.add(IBI)).divide(2.0); var ndvi = NDVI_cal(dataset_no_water); var wet...= Wet_cal(dataset_no_water); var visParams1 = { palette: '313695,4575b4,74add1,abd9e9,e0f3f8,ffffbf...= img_normalize(wet); dataset_no_water=dataset_no_water.addBands(unit_Wet.rename('Wet').toFloat()) var..., visParams2, "Wet");

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Google Earth Engine——USGSGAPPR2001波多黎各的详细植被和土地覆盖分类

alluvial evergreen forest 31 # Young secondary montane wet alluvial evergreen forest 32 # Montane...wet alluvial shrubland and woodland 33 # Mature secondary montane wet noncalcareous evergreen forest...montane wet noncalcareous evergreen elfin woodland cloud forest 38 # Young secondary montane wet noncalcareous...montane wet serpentine evergreen forest 41 # Young secondary montane wet serpentine evergreen forest...42 # Wet serpentine shrubland and woodland 43 # Montane wet evergreen abandoned and active coffee

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Python中复合数据类型（list，turple以及切片，循环等操作）

# 通过key（cat）访问; prints "cute" print 'cat' in d # 判断字典中是否有key; prints "True" d['fish'] = 'wet...' # 向字典加入对 print d['fish'] # Prints "wet" # print d['monkey'] # KeyError: 'monkey' not a key...with a default; prints "N/A" print d.get('fish', 'N/A') # Get an element with a default; prints "wet...** 2 for x in nums if x % 2 == 0} print even_num_to_square # Prints "{0: 0, 2: 4, 4: 16}" ---- 切片 python

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PIE-Engine APP：广东省生态遥感指数研究

本文以广东省为研究区，分别计算NDBSI\WET\NDVI\LST各个指数的的计算后遥感生态指数。...: 广州大学张三的组 * @Source : 航天宏图第四届 “航天宏图杯”PIE软件二次开发大赛云开发组三等奖获奖作品 * @Description : 1、计算LST、NDVI、WET...") //计算湿度指数 var maxWET = pie.Number(WET.reduceRegion(pie.Reducer.max(), gd, 500).get('wet')) //计算湿度指数最大值...var aveWET = pie.Number(WET.reduceRegion(pie.Reducer.mean(), gd, 500).get('wet')) //计算湿度指数平均值 var minWET...= pie.Number(WET.reduceRegion(pie.Reducer.min(), gd, 500).get('wet')) //计算湿度指数最小值 var noraveWET = aveWET.subtract

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GEE基于Landsat 8数据反演绿度热度湿度干度，并计算生态遥感指数代码分享

var ndvi = img.normalizedDifference(['B5', 'B4']); img = img.addBands(ndvi.rename('NDVI')) //计算WET...//WET var Wet = img.expression('B*(0.1509) + G*(0.1973) + R*(0.3279) + NIR*(0.3406) + SWIR1*(-0.7112...img.select(['B5']), 'SWIR1': img.select(['B6']), 'SWIR2': img.select(['B7']) }) Wet...= img_normalize(Wet) img = img.addBands(Wet.rename('WET').toFloat()) //计算NDBSI var ibi =...img_normalize(ndbsi) img = img.addBands(ndbsi.rename('NDBSI')) var L8_img = img.select(["NDVI","WET

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流水线物件检测数据集VOC+YOLO格式9255张26类别

Gps_sensor_container","Gps_sensor_pin_container","Hands","Hanging_screw_driver","Magnetic_tool","Multi_plate_wet_clutch...","Oil_bin","Piston","Plier","Spring_container","Vertical_Multi_plate_wet_clutch","Washer_container",...框数 = 3124 Hands 框数 = 28995 Hanging_screw_driver 框数 = 20080 Magnetic_tool 框数 = 3266 Multi_plate_wet_clutch...Oil_bin 框数 = 2003 Piston 框数 = 2707 Plier 框数 = 2565 Spring_container 框数 = 2707 Vertical_Multi_plate_wet_clutch

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GEE基于Landsat 8数据反演绿度热度湿度干度，并计算生态遥感指数代码分享

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ggplot2绘制美美的面积图

加载R包 library(tidyverse) library(ggtext) 导入数据 df <- read_tsv("data.xls") 数据筛选 ❝此处根据关键词将数据分为上下两个部分 ❞ wet_df...geom_line(position = "stack", size = 0.1, color = 'gray40') + # 给面积添加灰色轮廓 geom_area(data = wet_df..., alpha = 0.95) + geom_line(data = wet_df, position = "stack", size = 0.1, color = 'gray40')+

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Python Numpy学习教程（一）Python篇

Python Python是一种高级动态类型的多参数编程语言。Python代码经常被认为和伪代码(pseudocode)一样，因为它允许你在非常少的几行代码中表达非常强大的想法，同时可读性很高。...你可以通过在命令行运行python --version来检查你的Python版本。...cat']) # 从字典中根据键寻找对应值; 打印 "cute" 3print('cat' in d) # 判断字典中是否有给定的键; 打印 "True" 4d['fish'] = 'wet...' # 在字典中添加新的对 5print(d['fish']) # 打印 "wet" 6# print(d['monkey'])# KeyError: 'monkey'不是d中的键 7print...(d.get('monkey'), 'N/A') # 获取一个默认的元素; 打印 "N/A" 8print(d.get('fish'), 'N/A') # 获得一个默认的元素; 打印 "wet

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呕心沥血倾力巨制T-lnP图攻略——奥斯陆的气象生活

Wet-bulb Temperature (Tw) – 湿球温度: The minimum temperature at which a parcel of air can obtain by cooling...Tip: To find the wet-bulb temperature follow the moist adiabat through the lifting condensation level...7. wet-bulb pseudo temperature (Tsw) – 假湿球温度: Wet-bulb potential temperature, sometimes referred to as...8. wet-bulb pseudo potential temperature (Θsw) – 假湿球位温: Wet-bulb potential temperature, sometimes referred...have if, starting at the wet-bulb temperature, it were brought at the saturated adiabatic lapse rate

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瓶盖缺陷检测数据集VOC+YOLO格式8524张6类别

：6 标注类别名称(注意yolo格式类别顺序不和这个对应，而以labels文件夹classes.txt为准):["damaged","misplaced","no","normal","open","wet..."] 每个类别标注的框数： damaged 框数 = 741 misplaced 框数 = 596 no 框数 = 648 normal 框数 = 4848 open 框数 = 745 wet 框数 =

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Google Earth Engine ——美国LANDIFRE植被数据集包括。生物物理设置（BPS)环境场地潜力（ESP)现有植被冠层(EVC)现有植被高度（EVH）现有植被类型（EVT)数据集

557 #e2c2a2 Mediterranean California Subalpine Woodland 558 #aae3aa North Pacific Maritime Mesic-Wet...#ff9100 North Pacific Maritime Mesic Subalpine Parkland 726 #aae3aa North Pacific Maritime Mesic-Wet...#ff9100 North Pacific Maritime Mesic Subalpine Parkland 808 #aae3aa North Pacific Maritime Mesic-Wet...Riparian Forest and Shrubland 842 #bccfd4 North Pacific Montane Riparian Woodland and Shrubland - Wet...Meadow-Prairie-Marsh 2051 #d3ed26 North-Central Interior Wet Flatwoods 2052 #dc0000 Western Great

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点击加载更多

【CodeForces 621A】Wet Shark and Odd and Even

【CodeForces 621B】Wet Shark and Bishops

【CodeForces 621C】Wet Shark and Flowers

【CodeForces】#621A - Wet Shark and Odd and Even（水）

【CodeForces】#621B - Wet Shark and Bishops（思维）

关于马尔可夫链的一道题目

在没有数据的情况下使用贝叶斯定理设计知识驱动模型

（一）ROS系统入门 Getting Started with ROS 以Kinetic为主更新附课件PPT

【GEE】基于PCA的LANDSAT 8计算遥感生态指数（RSEI）

Google Earth Engine——USGSGAPPR2001波多黎各的详细植被和土地覆盖分类

Python中复合数据类型（list，turple以及切片，循环等操作）

PIE-Engine APP：广东省生态遥感指数研究

GEE基于Landsat 8数据反演绿度热度湿度干度，并计算生态遥感指数代码分享

流水线物件检测数据集VOC+YOLO格式9255张26类别

GEE基于Landsat 8数据反演绿度热度湿度干度，并计算生态遥感指数代码分享

ggplot2绘制美美的面积图

Python Numpy学习教程（一）Python篇

呕心沥血倾力巨制T-lnP图攻略——奥斯陆的气象生活

瓶盖缺陷检测数据集VOC+YOLO格式8524张6类别

Google Earth Engine ——美国LANDIFRE植被数据集包括。生物物理设置（BPS)环境场地潜力（ESP)现有植被冠层(EVC)现有植被高度（EVH）现有植被类型（EVT)数据集

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