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#Practice of K-D tree search library(FNN) #Define tha data points; (2,3),(5,4),(9,6),(4,7),(8,1),(7,2) points <-matrix(c( 2,3, 5,4, 9,6, 4,7, 8,1, 7,2 ), ncol=2, byrow=TRUE) colnames(points) <- c("x","y") #Plot the points for visualization plot(points, col="blue", pch=19, xlab="x", ylab="y", main="2D points and query") text(points, labels=1:nrow(points),pos=3) #Define query point (9,2) query_point <-matrix(c(9,2), ncol=2) points(query_point, col="red", pch=4, cex=2) text(query_point, labels="Query", pos=1) #Perform KD tree nearest neighbor search using FNN nn_result <- get.knnx(data=points, query=query_point, k=1) #Output the nearest neighbor and distance nearest_point <- points[nn_result$nn.index, ] distance <- nn_result$nn.dist cat("Nearest neighbor to (9,2) is: (", nearest_point[1], ",", nearest_point[2], ")\n") cat("Distance to nearest neighbor:", distance, "\n") #1.Construct 2-D tree #2.Return distance from nearest neighbor to query point #Distance=√(x1-x2)^2+(y1-y2)^2

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technical components

Se usaron los mismos archivos CSV distribuidos por el docente, tomando como base practicas previas para profundizar mas en la red neuronal y obtener un accuracy de al menos el 80%

TUGAS PRAKTIKUM PSD B

Tugas Praktikum 8 Analisis Regresi