NWAFU 生物统计实验二 R语言版

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原创 2025-03-21 08:43:48 398 阅读 AI 写同款· GEO 优化 ›

相机模型与特征点深度解析：从针孔投影链、K / R / t 到 ORB 描述子本文通过四个关键实测数据揭示相机投影的深层原理：投影非线性源于 $1/Z_c$ 项，外参角误差在像素域恒定（0.5°→4.3634 px），畸变模型缺失导致重投影误差高达45.507 px，而自由估计畸变虽降低 $\Delta f_x$ 却引入虚假参数。核心结论为：投影是射影映射，内参 $K$ 的五个参数分别对应焦距、主点与畸变，其误差传播依赖深度与视角。实验证明，仅靠重投影误差低不足以判断标定精度，必须结合物理合理性与模型匹配度。阅读详情

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#1
setwd(修改为你的工作路径或桌面路径)
feed_types <- c("A", "B", "C")
weight_gain_means <- c(36.8, 34.9, 21.3)
weight_gain_sds <- c(2.4, 2.7, 6.6)
weight_gain <- rnorm(3, mean = weight_gain_means, sd = weight_gain_sds)
food_consumption_means <- c(60.5, 56.9, 55.8)
food_consumption_sds <- c(3.1, 2.4, 1.5)
food_consumption <- rnorm(3, mean = food_consumption_means, sd = food_consumption_sds)
weight_food_ratio_means <- c(1.65, 1.64, 2.74)
weight_food_ratio_sds <- c(0.12, 0.13, 0.75)
weight_food_ratio <- rnorm(3, mean = weight_food_ratio_means, sd = weight_food_ratio_sds)
df_feed_stats <- data.frame(feed_types, weight_gain, food_consumption, weight_food_ratio)
#2
data()
df_chick <- ChickWeight
colnames(df_feed_stats)
colnames(df_chick)
df_merge <- merge(df_feed_stats, df_chick)

#3
na.fail(df_merge)
df_clean <- na.omit(df_merge)
names(df_clean)[5] <- 你的名字
library("openxlsx")
write.xlsx(df_clean, file = 你的文件名)  
#4
food_data <- df_clean$food_consumption[1:50]
food_asc <- sort(food_data)
food_desc <- sort(food_data, decreasing = TRUE)
print("升序排序后：")
print(food_asc)
print("降序排序后：")
print(food_desc)
str(df_clean)

setwd(你的工作目录)
feed <- c("A", "B", "C")
weight_up <- rnorm(3, mean = c(36.8, 34.9, 21.3), sd = c(2.4, 2.7, 6.6))
food <- rnorm(3, mean = c(60.5, 56.9, 55.8), sd = c(3.1, 2.4, 1.5))
weight_food <- rnorm(3, mean = c(1.65, 1.64, 2.74), sd = c(0.12, 0.13, 0.75))
data1 <- data.frame(feed, weight_up, food, weight_food)

data()
chicken<-ChickWeight
colnames(data1)
colnames(chicken)
data2 <- merge(data1, chicken)

na.fail(data2)
data3<-na.omit(data2)
names(data3)[5]<-"你的名字"
library("openxlsx")
write.xlsx(data3, file = "你的文件名")

food_data<-data3$food[1:100]
food_data_sort <- sort(food_data)
food_data_sort
food_data_sort <- sort(food_data,decreasing = T)
food_data_sort
str(data3)