66 lines
3.1 KiB
R
Executable File
66 lines
3.1 KiB
R
Executable File
##Final R assignment in Intro to Statistics course, fall semster.
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#+Written by Matan Horovitz (207130253) and Guy Amzaleg ()
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#+We have chosen a dataset of CPU and GPU performance trends since 2000 - as published on Kaggle:
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#+https://www.kaggle.com/datasets/michaelbryantds/cpu-and-gpu-product-data
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chip <- read.csv("/home/shmick/Downloads/chip_dataset.csv")
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#chip <- na.omit(chip)
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##BONUS: convert from EPOCH: as.Date(as.POSIXct(1100171890,origin = "1970-01-01"))
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#View(chip)
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##For question 1, we have chosen to examine which type of chip has examined the greater improvement over the years - GPU chips or CPU chips.
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#+As chip performance is most directly correlated with the number of transistors, we have measured the pace of development based on pace of
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#+increasing transistor count.
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CPU <- chip[chip$Type == 'CPU',]
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CPU <- subset(CPU, select= c(Product,Type,Release.Date,Process.Size..nm.,TDP..W.,Die.Size..mm.2.,Transistors..million.,Freq..MHz.))
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GPU <- chip[chip$Type == 'GPU',]
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GPU <- subset(GPU, select= c(Product,Type,Release.Date,Process.Size..nm.,TDP..W.,Die.Size..mm.2.,Transistors..million.,Freq..MHz.))
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#Calculate a crude 'performance factor' - the number of transistors multiplied by their frequency.
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#CPU["Performance Factor"])
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#Range of total transistor advancement
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max(CPU$Transistors..million.,na.rm=TRUE) - min(CPU$Transistors..million.,na.rm=TRUE)
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max(GPU$Transistors..million.,na.rm=TRUE) - min(GPU$Transistors..million.,na.rm=TRUE)
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#Omit chips with missing data
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#CPU <- na.omit(CPU)
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#GPU <- na.omit(GPU)
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##Iterate over date entries
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#for (i in 1:length(CPU$Release.Date)){print(i)}
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##Get date
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##Install the 'lubridate' package to deal with conversion to EPOCH time
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#install.packages('lubridate')
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#library(lubridate)
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#dates <- strptime(CPU$Release.Date,format="%Y-%m-%d")
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#as.integer(as.POSIXct(CPU$Release.Date))
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#posix_format_date <- c()
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#or (date in 1:length(CPU$Release.Date)){
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# cat("Date is", date)
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# human_format_date <- CPU$Release.Date[date]
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# print(human_format_date)
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# posix_format_date[date] <- strptime(human_format_date,format="%Y-%m-%d")
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#}
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#for (i in CPU$Release.Date){
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# print(i)
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#}
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##QUESTION 2: measure number of columns in our dataset and calculate a permutation and combination of
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#+that number, minus two, and 3.
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#Calculate total number of columns in our dataset
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#n <- ncol(kernel_commits)
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#View(n)
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##QUESTION 3: pick two categorcial variables (Chip type, foundry) and see whether they're dependent
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#+1. Probablity of chip type
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#+2. Probability of foundry
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#+3. Multiplty
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#Sample 1 variable from 'Type' column
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chip_type_sample <- sample(chip$Type,1)
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#Count how many times it appears in it's column
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p_chip_type_sample <- (length(which(chip$Type==chip_type_sample)))/length(chip$Type)
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chip_foundry_sample <- sample (chip$Foundry,1)
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p_chip_foundry_sample <- (length(which(chip$Foundry==chip_foundry_sample)))/length(chip$Foundry)
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chip_type_sample_matrix <- chip[chip$Type == chip_type_sample,]
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p_chip_type_foundry_sample <- (length(which(chip_type_sample_matrix$Foundry==chip_foundry_sample)))/length(chip_type_sample_matrix$Foundry)
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#p_victim_bastard <- p_neo_bastard * nrow(CPU )
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p_chip_type_foundry_sample * p_chip_type_sample |