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Second derivative of f(x) ≈ f(x−6)−2ƒ(x)+ƒ(x+8) Computationally, the function is represented as an array f(n), n = 1,2,3,... To numerically locate the minima of the second derivative, look at ƒ (n − 1) − 2ƒ(n) + f(n + 1) and find the values of n where it's most negative. Do this as an array function (avoid loops) Second derivative is minimized (most negative) when x = μ. Create a vector that represents f(n-1) - 2f(n) + f(n+1) and loop through values to find local minima. Those local minima will be the means for our Gaussians. We can also use the value of the second derivative to estimate the standard deviations.

Second derivative of f(x) ≈ f(x−6)−2ƒ(x)+ƒ(x+8) Computationally, the function is represented as an array f(n), n = 1,2,3,... To numerically locate the minima of the second derivative, look at ƒ (n − 1) − 2ƒ(n) + f(n + 1) and find the values of n where it's most negative. Do this as an array function (avoid loops) Second derivative is minimized (most negative) when x = μ. Create a vector that represents f(n-1) - 2f(n) + f(n+1) and loop through values to find local minima. Those local minima will be the means for our Gaussians. We can also use the value of the second derivative to estimate the standard deviations.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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PLEASE DO IN RSTUDIO (R PROGRAMMING) Please show each step in the code. 

Data Set:

    890.776 890.519 890.263 890.006 889.749 889.493 889.236 888.979
-4.25909 -6.9024 9845 9608 9782 9708 9661 9609 9832 9753
-4.25909 -6.4544 9507 9340 9337 9325 9441 9300 9470 9143
-4.25909 -6.0064 9576 9201 9252 9238 9217 9298 9217 9224
-4.25909 -5.5584 9604 9301 9467 9279 9457 9438 9395 9310
ƒ(x−8)−2ƒ(x)+ƒ(x+6) Second derivative of f(x) Computationally, the function is represented as an array f(n), n = 1,2,3,... To numerically locate the minima of the second derivative, look at f(n-1) − 2ƒ(n) + f(n + 1) and find the values of n where it's most negative. Do this as an array function (avoid loops) Second derivative is minimized (most negative) when x = μ. Create a vector that represents f(n-1) − 2f(n) + f(n + 1) and loop through values to find local minima. Those local minima will be the means for our Gaussians. We can also use the value of the second derivative to estimate the standard deviations.
Transcribed Image Text:ƒ(x−8)−2ƒ(x)+ƒ(x+6) Second derivative of f(x) Computationally, the function is represented as an array f(n), n = 1,2,3,... To numerically locate the minima of the second derivative, look at f(n-1) − 2ƒ(n) + f(n + 1) and find the values of n where it's most negative. Do this as an array function (avoid loops) Second derivative is minimized (most negative) when x = μ. Create a vector that represents f(n-1) − 2f(n) + f(n + 1) and loop through values to find local minima. Those local minima will be the means for our Gaussians. We can also use the value of the second derivative to estimate the standard deviations.
Create an R script that: • Reads in the data as an array from a .txt file (make the txt file name be a variable) Extract the first row as the set of wavelengths (store as a vector) and remove first row from data. Loop through the rows of the array. Find minima of second derivative as described above Store the minimum values and their indices (n) in two different arrays.
Transcribed Image Text:Create an R script that: • Reads in the data as an array from a .txt file (make the txt file name be a variable) Extract the first row as the set of wavelengths (store as a vector) and remove first row from data. Loop through the rows of the array. Find minima of second derivative as described above Store the minimum values and their indices (n) in two different arrays.
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