Done!

R_Final_Tasks_Statistics.R

@@ -11,9 +11,9 @@ chip <- read.csv("/home/shmick/Downloads/chip_dataset.csv")
 #+As chip performance is most directly correlated with the number of transistors, we have measured the pace of development based on pace of 
 #+increasing transistor count.
 CPU <- chip[chip$Type == 'CPU',]
-CPU <- subset(CPU, select= c(Product,Type,Release.Date,Process.Size..nm.,TDP..W.,Die.Size..mm.2.,Transistors..million.,Freq..MHz.))
+#CPU <- subset(CPU, select= c(Product,Type,Release.Date,Process.Size..nm.,TDP..W.,Die.Size..mm.2.,Transistors..million.,Freq..MHz.))
 GPU <- chip[chip$Type == 'GPU',]
-GPU <- subset(GPU, select= c(Product,Type,Release.Date,Process.Size..nm.,TDP..W.,Die.Size..mm.2.,Transistors..million.,Freq..MHz.))
+#GPU <- subset(GPU, select= c(Product,Type,Release.Date,Process.Size..nm.,TDP..W.,Die.Size..mm.2.,Transistors..million.,Freq..MHz.))
 #Calculate a crude 'performance factor' - the number of transistors multiplied by their frequency.
 #CPU["Performance Factor"])
 #Range of total transistor advancement
@@ -55,12 +55,106 @@ max(GPU$Transistors..million.,na.rm=TRUE) - min(GPU$Transistors..million.,na.rm=
 #+3. Multiplty
 
 #Sample 1 variable from 'Type' column
-chip_type_sample <- sample(chip$Type,1)
+sampled_type <- sample(chip$Type,1)
 #Count how many times it appears in it's column
-p_chip_type_sample <- (length(which(chip$Type==chip_type_sample)))/length(chip$Type)
-chip_foundry_sample <- sample (chip$Foundry,1)
-p_chip_foundry_sample <- (length(which(chip$Foundry==chip_foundry_sample)))/length(chip$Foundry)
-chip_type_sample_matrix <- chip[chip$Type == chip_type_sample,]
-p_chip_type_foundry_sample <- (length(which(chip_type_sample_matrix$Foundry==chip_foundry_sample)))/length(chip_type_sample_matrix$Foundry)
-#p_victim_bastard <- p_neo_bastard * nrow(CPU )
-p_chip_type_foundry_sample * p_chip_type_sample
+p_sampled_type <- (length(which(chip$Type==sampled_type)))/length(chip$Type)
+sampled_foundry <- sample(chip$Foundry,1)
+p_sampled_foundry <- (length(which(chip$Foundry==sampled_foundry)))/length(chip$Foundry)
+sampled_type_matrix <- chip[chip$Type == sampled_type,]
+p_sampled_foundry_in_sampled_type <- (length(which(sampled_type_matrix$Foundry==sampled_foundry)))/length(sampled_type_matrix$Foundry)
+p_sampled_chip_and_foundry <- p_sampled_foundry_in_sampled_type * p_sampled_type
+
+if (p_sampled_chip_and_foundry == (p_sampled_type * p_sampled_foundry)){
+  print("Independent")
+}else{
+  print("Dependent")
+}
+
+#Question 4 - 'Amazing'
+GPU <- na.omit(GPU)
+fp16_gflops <- na.omit(GPU$FP16.GFLOPS)
+#Get total range of FP.16 GFLOPS
+fp16_range <- as.numeric(sprintf("%.2f",(max(GPU$FP16.GFLOPS,na.rm=TRUE))-min(GPU$FP16.GFLOPS,na.rm=TRUE)))
+fp16_low_threshold <- fp16_range / 3
+fp16_medium_threshold <- fp16_low_threshold *2
+#Create empty vector named 'amazing'
+amazing <-c()
+#Iterate over all numbers from 1 to the length of the vector
+for (i in 1:length(fp16_gflops))
+{
+  fp16_gflop <- fp16_gflops[i]
+  #If the number is greater or equal to 1 AND ALSO (&) smaller or equal to 3...
+  if(fp16_gflop <= fp16_low_threshold)
+    #    ^ this bit is important
+  {
+    cat(fp16_gflop, "is low\n")
+    #Add "low" to list called 'Amazing'
+    amazing[i] <- "low"
+    #Once this condition is satisfied, move on to next item in loop (if on 1, move on to 2, etc)
+    next
+  }
+  #If the number is greater or equal to 3 AND ALSO (&) smaller or equal to 6...
+  else if(fp16_gflop > fp16_low_threshold & fp16_gflop <= fp16_medium_threshold  )
+    #             ^ this is like two IF's
+  {
+    cat(fp16_gflop, "is medium\n")
+    amazing[i] <- "medium"
+    next
+  } else if(fp16_gflop > fp16_medium_threshold) {
+    cat(fp16_gflop, "is high\n")
+    amazing[i] <- "high"
+    next 
+  } else {
+  cat(fp16_gflop, "is unknown\n")
+  }
+}
+amazing
+GPU["Amazing"] <- amazing
+#Question 5
+sorted_fp16_gflops <- sort(fp16_gflops)
+fp16_gflops_length <- length(fp16_gflops)
+#If the length of the sorted vector is divisble by 2...
+if ((fp16_gflops_length %% 2) == 0) {
+  print("Dataset is even")
+  #... create a vector of the 2 middle elements...
+  fp16_gflops_medians <- c((fp16_gflops_length/2),((fp16_gflops_length/2)+1))
+  #... and calculate their average; that is the mean.
+  fp16_gflops_median <- mean(sorted_fp16_gflops[fp16_gflops_medians])
+  #                  ^ This is a vector of the 2 middle spots in our even vector
+} else #< If the length of the sorted vector is odd...
+{
+  print("Vector is odd")
+  #Get the index of the median number by adding 1 to the total count, and divide by half.
+  fp16_gflops_median_index <- (((fp16_gflops_length + 1)/2))
+  #The median is the number in the index we figured out earlier; pull it from the sorted vector.
+  fp16_gflops_median <- sorted_fp16_gflops[fp16_gflops_median_index]
+}
+cat("Median is:", fp16_gflops_median)
+#Question 6
+sampled_fp_32_gflops <- c()
+for (i in 1:3){
+  cat("On ", i, "\n")
+  sampled_fp_32_gflop <- sample(chip$FP32.GFLOPS,1)
+  while (sampled_fp_32_gflop < 0 | is.na(sampled_fp_32_gflop))
+  {
+    cat("Sampled value ", sampled_fp_32_gflops, "is negative. Retrying...\n")
+    sampled_fp_32_gflop <- sample(chip$FP32.GFLOPS,1)
+  } 
+  
+  sampled_fp_32_gflops[i] <- sampled_fp_32_gflop
+}
+pnorm(sampled_fp_32_gflops[1],mean = sampled_fp_32_gflops[2], sd = sqrt(sampled_fp_32_gflops[3]))
+
+#Question 7 
+fp64_gflops <- na.omit(GPU$FP64.GFLOPS)
+mean(fp64_gflops)
+var(fp64_gflops)
+zscore <- (fp64_gflops - mean(fp64_gflops)) / sd(fp64_gflops)
+#fp64_gflops_trans <- (fp64_gflops*2 + 16)
+zscore_lin_trans <- ( ( (1/sd(fp64_gflops) * 2000 ) * fp64_gflops ) - ( mean(fp64_gflops)/sd(fp64_gflops) ) )
+#                                          ^ THIS is the linear transformation.
+zscore_non_lin_trans <- ( ( (1/sd(fp64_gflops) * (fp64_gflops) ^ -0.7 ) * fp64_gflops ) - ( mean(fp64_gflops)/sd(fp64_gflops) ) )
+
+plot(zscore_lin_trans,zscore_non_lin_trans,col = blue)
+#plot(zscore,zscore_lin_trans)
+doubled_zscore <- zscore * 2