code created by DeepSeek

Decision tree with estimated costs and probabilities if META were to choose or not to go with Nuclear power to meet its speculated 4GWt power consumption by 2030

Geostats 2025

Mapa de registros de Tremarctos ornatus hasta abril del 2025 tomando la información de GBIF © Daniel Santiago Ballesteros Rocha (dballesterosr@unal.edu.co)

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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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