feat: w3 done

.gitignore

@@ -2,3 +2,4 @@
 **/.Rhistory
 **/.RData
 **/.Ruserdata
+.Rproj.user

w3/README.rmd

@@ -0,0 +1,432 @@
+# Übungsblatt 3 - Evolutionary Algorithms (Paul Helstab & Yannik Bretschneider)
+
+## Loading Data
+
+```{r setwd}
+# Get the current working directory
+current_directory <- getwd()
+
+# Define the folder name to check
+folder_name <- "w3"
+
+# Construct the full path to the folder
+folder_path <- file.path(current_directory, folder_name)
+
+# Check if the folder exists (means most likely in the repo root folder)
+if (dir.exists(folder_path)) {
+    setwd(folder_path)
+    cat("Changed working directory to:", getwd(), "\n")
+} else {
+    cat("Folder 'w3' does not exist in the current working directory.
+Staying where we are\n")
+}
+```
+
+
+```{r read_data}
+file_path <- "./data1.txt"
+
+print(paste0("read data from '", file_path, "'"))
+data <- read.table(
+    file_path,
+    header = FALSE,
+    sep = " ",
+    # colClasses = c("numeric", "numeric", "integer", "integer")
+)
+colnames(data) <- c("x1", "x2", "x_bias", "label")
+
+
+evaluation_set_idx <- c(90:109)
+```
+
+```r
+# possible, but unnecessary here:
+# grab first 10 as evaluation set
+evaluation_set <- data[evaluation_set_idx, ]
+
+# grab the rest as training set
+training_set <- data[-evaluation_set_idx, ]
+```
+
+## Aufgabe 1: Perzeptron
+
+```{r peceptron-impl}
+# randomized order
+random_order <- sample(nrow(data))
+vecs <- data[random_order, 1:3]
+classes <- data[random_order, 4]
+
+weights <- c(0, 0, 0)
+
+# matrix for logging weights
+wlog <- matrix(weights, ncol = 3)
+
+infer <- function(x, wghts) {
+    return(sign(x %*% wghts))
+}
+
+perceptron_infer <- function(x) {
+    return(infer(x, weights))
+}
+
+while (TRUE) {
+    mispredictions <- 0
+    for (i in seq_len(nrow(vecs))) {
+        vec <- as.numeric(vecs[i, ])
+        class <- classes[i]
+
+        # print(vec %*% weights)
+
+        error <- as.numeric(class - perceptron_infer(vec))
+        if (error != 0) {
+            mispredictions <- mispredictions + 1
+
+            print("adjusting")
+            weights <- weights + error * vec
+            wlog <- rbind(wlog, weights)
+        }
+    }
+
+
+    if (mispredictions == 0) {
+        print("No more mispredictions")
+        break
+    } else {
+        print(paste0("Misclassifications: ", mispredictions))
+    }
+}
+
+print("NOTE: for more interesting plots, run this chunk multiple times until multiple \"adjusting\" can be seen in the output")
+```
+
+```{r apply-to-data}
+perceptron_reinfer <- t(sapply(seq_len(nrow(data)), function(i) {
+    return(c(as.numeric(data[i, 1:3]), perceptron_infer((as.numeric(data[i, 1:3])))))
+}))
+
+# perceptron_reinfer
+```
+
+
+### Plots
+
+The plots show the datapoints on a 2D plot, with the colors showing the classes (`green := 1` and `red := -1`)  
+The separation plane defined by the normal of the weight vector is shown in increasing opacities, until the final 
+weights are reached.
+
+```{r plot-weights}
+perceptron_plot <- function() {
+    plot(data$x1, data$x2,
+        col = ifelse(data$label == 1, "green", "red"),
+        pch = 16, xlab = "x1", ylab = "x2", xlim = c(-5, 5), ylim = c(-5, 5)
+    )
+
+    legend("topright",
+        legend = c("Class 1", "Class -1"),
+        col = c("green", "red"), pch = 16
+    )
+
+    # Plot the separation planes
+    num_steps <- nrow(wlog)
+    alpha_step <- 1 / num_steps
+
+    for (i in 1:num_steps) {
+        a <- wlog[i, 1]
+        b <- wlog[i, 2]
+        c <- wlog[i, 3]
+
+        # Calculate the line coordinates
+        x <- seq(-5, 5, length.out = 100)
+        y <- -(a * x + c) / b
+
+        # Determine the line color based on the iteration
+        alpha <- i * alpha_step
+        line_color <- rgb(0, 0, 1, alpha)
+
+        # Plot the line
+        lines(x, y, col = line_color)
+    }
+
+    # Plot the final separation plane
+    a <- weights[1]
+    b <- weights[2]
+    c <- weights[3]
+
+    x <- seq(-5, 5, length.out = 100)
+    y <- -(a * x + c) / b
+
+    lines(x, y, col = "blue", lwd = 2)
+}
+
+perceptron_plot()
+```
+
+```{r plot-weights-pdf}
+pdf("perceptron-plot.pdf", width = 8, height = 6)
+perceptron_plot()
+dev.off()
+```
+
+
+
+## Aufgabe 2: Perzeptron Evolutionary Algorithm (EA)
+
+
+```{r peceptron-ea-impl}
+# randomized order
+ea_random_order <- sample(nrow(data))
+ea_vecs <- data[ea_random_order, 1:3]
+ea_classes <- data[ea_random_order, 4]
+
+NUM_INDIVIDUALS <- 3
+MUTATE_ALL_GENES <- FALSE
+MUTATE_STEP <- function() {
+    return(0.5)
+}
+
+# MUTATE_STEP <- function() {
+#     return(runif(1, min = -1, max = 1)[1])
+# }
+
+weights <- matrix(0, ncol = 3, nrow = NUM_INDIVIDUALS)
+
+# matrix for logging weights
+ea_wlog <- matrix(0, ncol = 3)
+
+fittest <- c(0, 0, 0)
+
+
+mutate <- function(genes, step_size_fn, mutate_all) {
+    if (mutate_all == TRUE) {
+        for (i in seq_along(genes)) {
+            step <- step_size_fn() * sample(c(1, -1), size = 1)[1] # mutation in step_size, -step_size or not at all
+            genes[i] <- genes[i] + step
+        }
+    } else {
+        idx_to_mutate <- sample(seq_along(genes), size = 1)[1]
+        genes[idx_to_mutate] <- genes[idx_to_mutate] + step_size_fn() * sample(c(1, -1), size = 1)[1] # mutation in step_size or not AT all
+    }
+
+    return(genes)
+}
+
+infer <- function(x, wghts) {
+    return(sign(x %*% wghts))
+}
+
+fitness <- function(wghts) {
+    mispredictions <- 0
+
+
+    for (i in seq_len(nrow(ea_vecs))) {
+        vec <- as.numeric(ea_vecs[i, ])
+        class <- ea_classes[i]
+
+        # print(vec %*% weights)
+
+        error <- as.numeric(class - infer(vec, wghts))
+        if (error != 0) {
+            mispredictions <- mispredictions + 1
+        }
+    }
+
+    return(mispredictions)
+}
+
+while (TRUE) {
+    i_fittest <- weights[1, ]
+    i_fit <- fitness(weights[1, ])
+
+    for (individual in 2:NUM_INDIVIDUALS) {
+        this_fitness <- fitness(weights[individual, ])
+        if (this_fitness < i_fit) {
+            i_fittest <- weights[individual, ]
+            i_fit <- this_fitness
+        }
+    }
+
+    # add fittest to weights
+    ea_wlog <- rbind(ea_wlog, i_fittest)
+
+    if (i_fit == 0) {
+        print("No more mispredictions")
+        fittest <- i_fittest
+        break
+    } else {
+        print(paste0("Misclassifications: ", i_fit))
+
+        for (individual in 1:NUM_INDIVIDUALS) {
+            weights[individual, ] <- mutate(i_fittest, step_size_fn = MUTATE_STEP, mutate_all = MUTATE_ALL_GENES)
+        }
+    }
+}
+```
+
+
+```{r ea-apply-to-data}
+ea_reinfer <- t(sapply(seq_len(nrow(data)), function(i) {
+    return(c(as.numeric(data[i, 1:3]), infer((as.numeric(data[i, 1:3])), fittest)))
+}))
+
+# ea_reinfer
+```
+
+### Plots
+
+The plots show the datapoints on a 2D plot, with the colors showing the classes (`green := 1` and `red := -1`)  
+The separation plane defined by the normal of the weight vector is shown in increasing opacities, until the final 
+weights are reached.
+
+```{r ea-plot-weights}
+ea_plot <- function() {
+    plot(data$x1, data$x2,
+        col = ifelse(data$label == 1, "green", "red"),
+        pch = 16, xlab = "x1", ylab = "x2", xlim = c(-5, 5), ylim = c(-5, 5)
+    )
+
+    legend("topright",
+        legend = c("Class 1", "Class -1"),
+        col = c("green", "red"), pch = 16
+    )
+
+    # Plot the separation planes
+    num_steps <- nrow(ea_wlog)
+    alpha_step <- 1 / num_steps
+
+    for (i in 1:num_steps) {
+        a <- ea_wlog[i, 1]
+        b <- ea_wlog[i, 2]
+        c <- ea_wlog[i, 3]
+
+        # Calculate the line coordinates
+        x <- seq(-5, 5, length.out = 100)
+        y <- -(a * x + c) / b
+
+        # Determine the line color based on the iteration
+        alpha <- i * alpha_step
+        line_color <- rgb(0, 0, 1, alpha)
+
+        # Plot the line
+        lines(x, y, col = line_color)
+    }
+
+    # Plot the final separation plane
+    a <- fittest[1]
+    b <- fittest[2]
+    c <- fittest[3]
+
+    x <- seq(-5, 5, length.out = 100)
+    y <- -(a * x + c) / b
+
+    lines(x, y, col = "blue", lwd = 2)
+}
+
+ea_plot()
+```
+
+
+```{r plot-weights-pdf}
+pdf("ea-plot_static-delta_one.pdf", width = 8, height = 6)
+ea_plot()
+dev.off()
+```
+
+### Evaluation
+
+Gaussian (Mutate All) with a high number of individuals generally acheives a correct result first-try. In our very constrained individuum pool tests with 3 individuals, this also seems to be the best choice for parameters for the EA. Its efficacy seems to increase even more with a larger gene pool. 
+
+
+## Aufgabe 3: Exploration vs Exploitation
+
+```{r setup}
+# generate a random binary vector of length 10
+generate_individual <- function() {
+    sample(c(0, 1), 10, replace = TRUE)
+}
+
+# 1-point crossover
+one_point_crossover <- function(parent1, parent2) {
+    point <- sample(1:9, 1)
+    child1 <- c(parent1[1:point], parent2[(point + 1):10])
+    child2 <- c(parent2[1:point], parent1[(point + 1):10])
+    return(list(child1, child2))
+}
+
+# 2-point crossover
+two_point_crossover <- function(parent1, parent2) {
+    points <- sort(sample(1:9, 2))
+    child1 <- c(parent1[1:points[1]], parent2[(points[1] + 1):points[2]], parent1[(points[2] + 1):10])
+    child2 <- c(parent2[1:points[1]], parent1[(points[1] + 1):points[2]], parent2[(points[2] + 1):10])
+    return(list(child1, child2))
+}
+
+# uniform crossover
+uniform_crossover <- function(parent1, parent2) {
+    mask <- sample(c(0, 1), 10, replace = TRUE)
+    child1 <- ifelse(mask == 1, parent1, parent2)
+    child2 <- ifelse(mask == 1, parent2, parent1)
+    return(list(child1, child2))
+}
+
+# Function to convert binary vector to decimal
+binary_to_decimal <- function(binary_vector) {
+    sum(binary_vector * 2^(rev(seq_along(binary_vector) - 1)))
+}
+```
+
+
+```{r initialize_parents}
+# init parents
+parent1 <- generate_individual()
+parent2 <- generate_individual()
+
+print(parent1)
+print(parent2)
+
+# Set num gens
+num_generations <- 10000
+
+# for tracking unique solutions
+unique_solutions <- list()
+unique_solutions[[binary_to_decimal(parent1)]] <- TRUE
+unique_solutions[[binary_to_decimal(parent2)]] <- TRUE
+```
+
+
+```{r run_genetic_algorithm}
+for (generation in 1:num_generations) {
+    # Choose crossover method
+    crossover_method <- sample(c("one_point", "two_point", "uniform"), 1)
+
+    if (crossover_method == "one_point") {
+        children <- one_point_crossover(parent1, parent2)
+    } else if (crossover_method == "two_point") {
+        children <- two_point_crossover(parent1, parent2)
+    } else {
+        children <- uniform_crossover(parent1, parent2)
+    }
+
+
+
+    # Replace
+    parent1 <- children[[1]]
+    parent2 <- children[[2]]
+
+    # Track sols
+    unique_solutions[[binary_to_decimal(parent1)]] <- TRUE
+    unique_solutions[[binary_to_decimal(parent2)]] <- TRUE
+}
+
+# Calculate number of unique solutions observed
+num_unique_solutions <- length(unique_solutions)
+
+# Calculate number of possible solutions covered
+num_possible_solutions <- 2^10
+num_solutions_covered <- length(unique_solutions)
+
+# Print results
+cat("Number of unique solutions observed:", num_unique_solutions, "\n")
+cat("Number of possible solutions covered:", num_solutions_covered, "\n")
+cat("Percentage of 10-bit numbers covered:", round(num_solutions_covered / num_possible_solutions * 100, 2), "%\n")
+```
+

w3/data1.txt

@@ -0,0 +1,200 @@
+-1.32897014671217 -2.36785102664075 -1 1
+-2.15970367170725 -1.41686449781219 -1 1
+-1.38963684686937 -0.939314504497346 -1 1
+-2.39486126072160 0.156941452755162 -1 1
+-2.06264712461684 -0.804412097745957 -1 1
+-1.59995934139993 -2.07087560267702 -1 1
+-0.301759430691359 -0.780246221467475 -1 1
+-1.30880048251936 -0.655267889174166 -1 1
+-2.13821265199027 -3.26960613438387 -1 1
+-0.725161331033519 -0.677840073724945 -1 1
+-2.73393355319214 -0.76718258286048 -1 1
+-1.54347592440629 -0.712018637866198 -1 1
+-2.56545772113774 -2.86936228227077 -1 1
+-1.43201933507797 -2.13302840936145 -1 1
+-2.07330667024688 -3.72123784804042 -1 1
+-2.84033507635568 -0.243184822799128 -1 1
+-1.47403941210529 -0.549328960464295 -1 1
+-1.83738457125925 -2.66717286904658 -1 1
+-2.27871321216998 -2.98058889038791 -1 1
+-1.95615460430846 -1.67004701470848 -1 1
+-2.60073487762731 -2.20452775815597 -1 1
+-0.26734293595561 -1.71331299152070 -1 1
+-3.47839627261639 -2.83115791470682 -1 1
+-1.19695437101501 -0.945609834844714 -1 1
+-2.78998139170657 0.257675391308978 -1 1
+-2.12464531272405 0.777501391183755 -1 1
+-2.31932391039648 -1.86277899921771 -1 1
+-5.54482177865753 -2.70213117761077 -1 1
+-2.78021553510943 -3.03301792798881 -1 1
+-1.60768417956298 -1.74172015222199 -1 1
+-3.61446849343074 -1.80335879930914 -1 1
+-3.23422019981727 -2.70249791326027 -1 1
+-3.02682979962044 -1.74278869754663 -1 1
+-2.41276478154580 -0.776567065003342 -1 1
+-1.75552935518460 -1.91038140781957 -1 1
+-0.139451116184016 -1.18482537954623 -1 1
+-2.95650175314386 -1.29101568834187 -1 1
+-2.9489793179245 -1.61904473503749 -1 1
+-2.60315833182049 -3.18621980659633 -1 1
+-1.46831479870983 -3.80905675128548 -1 1
+-0.905005395061582 -2.03556983376913 -1 1
+-0.901078142728159 -0.825017845358332 -1 1
+-0.665652556519657 -1.32550835429996 -1 1
+-1.46112641743637 -1.97574413713737 -1 1
+-1.06457534745575 -2.785521609747 -1 1
+-1.34420271056785 -1.06666579289930 -1 1
+-2.07631856650808 -1.93542983825108 -1 1
+-2.35690175063823 -3.26600979191016 -1 1
+-1.7056897160519 -3.04297084229814 -1 1
+-2.80704309897473 -1.53106686645482 -1 1
+-2.44711856977661 -2.5849302819368 -1 1
+-1.51065176293650 -0.86509385564769 -1 1
+-1.94090434314101 -0.634401545904208 -1 1
+-2.04270534190753 -0.361097103415342 -1 1
+-1.06607471507957 -3.29065512487734 -1 1
+-3.24060617216703 -2.26876252288359 -1 1
+-0.0486425864884459 -1.13673076708325 -1 1
+-0.266739979422662 -0.878999257393973 -1 1
+-0.420637663314567 -0.482675855002418 -1 1
+-1.06406981568800 -4.17713862737802 -1 1
+-0.625197602089241 -2.91030552103365 -1 1
+-3.27562750609849 -2.30760870403296 -1 1
+-0.499947053765473 -3.29272300439147 -1 1
+-2.91674268064002 -1.68191356080486 -1 1
+-2.17888247623848 -0.662447607788639 -1 1
+-2.09290120372439 -0.593566611901163 -1 1
+-1.25264885418333 -3.199747175357 -1 1
+-2.00339086767013 -0.708453667782978 -1 1
+-1.36640247016023 -0.609207709185745 -1 1
+-0.911401862275114 -2.55455598535213 -1 1
+-1.64339071909895 -2.70101342303568 -1 1
+-1.71423981174520 -2.33624402853916 -1 1
+0.199702636780847 -1.83962146056716 -1 1
+-1.18400256670017 -2.30370589782562 -1 1
+-2.80977943163309 -0.908178300835772 -1 1
+-2.13569173985164 -0.91407304459949 -1 1
+-0.700888732479868 -1.74846788054676 -1 1
+-3.14027577143708 -0.902886138153785 -1 1
+-0.681225799170511 -0.525783857770332 -1 1
+-2.08732062127811 -1.89701037917116 -1 1
+-0.0586057242746669 -4.04053972551628 -1 1
+-1.88077968783775 0.421666644295287 -1 1
+-1.37795480435589 -1.77199347049285 -1 1
+-1.41637459799429 -2.44011696127162 -1 1
+-2.77060878833344 -0.929345155427266 -1 1
+-2.19214219615774 -1.98905330321631 -1 1
+-2.52816178990139 -2.2585972967922 -1 1
+-2.16234859219211 -1.60438927644781 -1 1
+-1.62177037851373 -0.99010930185921 -1 1
+-3.23318182763954 -1.13658991149595 -1 1
+-0.372235271791896 -2.34620587435379 -1 1
+-1.5001555577351 -1.95434020917647 -1 1
+-3.52894478305670 -2.96387692953025 -1 1
+-3.6191496287414 -0.799179012881697 -1 1
+-1.95049705463410 -1.76753962185022 -1 1
+-2.99928621186237 -1.39904916882386 -1 1
+-2.00475587442074 -2.36844162821365 -1 1
+-0.830570944902423 -1.45228655753697 -1 1
+-1.49328010078687 -2.08341021079596 -1 1
+-1.99969145454271 -1.06885729770145 -1 1
+2.49460626439645 3.86875369750027 -1 -1
+0.7446494499116 1.68808804411077 -1 -1
+1.71014916961483 1.73205312306135 -1 -1
+2.53359800973500 1.6784603245221 -1 -1
+4.36539754555225 2.68376377601958 -1 -1
+1.30100189213241 0.393578903079953 -1 -1
+3.39025005470382 3.81407573113482 -1 -1
+2.99425229125324 1.55248460007409 -1 -1
+-0.697405479258973 2.66052071663056 -1 -1
+1.05485413438518 1.43026328316951 -1 -1
+2.15499286281072 2.80396478089847 -1 -1
+1.13383276710016 1.16636578938430 -1 -1
+2.40583658837318 1.71309249518999 -1 -1
+3.9529836684296 -0.0306622708202284 -1 -1
+1.44673688777537 1.69536150393561 -1 -1
+2.40653572484163 2.53986102442878 -1 -1
+0.927093361660263 2.05434503667819 -1 -1
+2.13211058846022 0.780770245015111 -1 -1
+1.52942807748731 -0.384411036254697 -1 -1
+0.0374065135699719 3.71412296025181 -1 -1
+1.32860906911755 2.4624846210011 -1 -1
+3.11790204244201 4.23424681600907 -1 -1
+1.47550988790566 1.1318875058157 -1 -1
+1.76354867452616 0.186487030093577 -1 -1
+2.72517351969596 0.867467761927136 -1 -1
+1.64871907711219 1.14947956824857 -1 -1
+3.35728840379298 3.39193733666552 -1 -1
+1.97314223473543 0.71936438292462 -1 -1
+2.46627878877807 1.85228201801042 -1 -1
+1.82782122930369 0.662112012577584 -1 -1
+0.469136899706291 2.69385034831914 -1 -1
+2.95906527151821 1.34793880046152 -1 -1
+3.1845846978969 1.90347273893573 -1 -1
+1.62972843753454 1.88240236488746 -1 -1
+1.84880143044407 1.08902891549876 -1 -1
+2.74003682637714 3.28858597224097 -1 -1
+1.54579953010360 2.58652641559206 -1 -1
+2.41806254601694 1.41997311235272 -1 -1
+0.679066628438395 2.5492871531371 -1 -1
+3.53627392920231 2.20395858016939 -1 -1
+1.91575617514485 2.04417898782122 -1 -1
+1.99849496533958 1.34313999800502 -1 -1
+3.73338748817565 2.7673871731482 -1 -1
+2.35097932569732 3.09463656920031 -1 -1
+2.44118802836801 2.77962382569914 -1 -1
+2.72291823080543 4.11301272621089 -1 -1
+-0.333578934240685 1.93864004097092 -1 -1
+1.45281983530088 1.77630338471216 -1 -1
+4.06662985060252 2.79810284586335 -1 -1
+1.18796770270894 1.94600292544487 -1 -1
+0.690665017500061 3.21726119606832 -1 -1
+2.78069373531768 1.98025053530047 -1 -1
+2.07146143234442 2.77246826065373 -1 -1
+2.36549947930286 2.31182063191906 -1 -1
+1.4782493446176 1.98015787175957 -1 -1
+0.585216947594101 1.22131489755131 -1 -1
+1.85336420511426 0.707882262279918 -1 -1
+2.41381294874122 2.24766717770949 -1 -1
+2.39771241502864 1.77055374509899 -1 -1
+0.604267049505909 3.16280325091495 -1 -1
+2.81450596467485 1.14183730585994 -1 -1
+2.93364913348434 2.64917329100802 -1 -1
+3.18414663995059 3.16608797066945 -1 -1
+3.25191092293097 3.42335827192670 -1 -1
+2.2453247048103 1.59789384315062 -1 -1
+0.875192156027067 1.54233584846762 -1 -1
+2.89266085813154 1.29408855207970 -1 -1
+0.171910769280119 3.71229856331406 -1 -1
+0.519019904060457 1.22505957089452 -1 -1
+1.81434871964992 1.27449969743080 -1 -1
+2.57908019548281 2.02392243783863 -1 -1
+1.70639994544607 0.581553546693116 -1 -1
+2.35788618976732 0.611170547764614 -1 -1
+2.25599283045020 1.32790185796492 -1 -1
+1.35040643794059 2.07276219536503 -1 -1
+1.39329211427557 2.06473061135590 -1 -1
+1.33088217468705 2.39043191559914 -1 -1
+1.73118239651758 0.655853745458392 -1 -1
+2.70919473877065 1.94105221553915 -1 -1
+2.1448373798777 2.19455127482444 -1 -1
+0.974221061163073 0.780324226340574 -1 -1
+1.60717332452036 2.90271704500523 -1 -1
+1.78505625028714 0.29413215901734 -1 -1
+1.26508124491513 2.92214327142116 -1 -1
+0.653893256808685 1.48139537994700 -1 -1
+1.94673646103508 1.51013334653874 -1 -1
+1.37651122485238 3.31676717137167 -1 -1
+0.760451278123538 0.979923184771868 -1 -1
+1.28292094213002 2.66135701016817 -1 -1
+0.536342053807597 1.85100087363587 -1 -1
+1.18225844069278 0.39263971309868 -1 -1
+2.5717715638596 2.84462847265310 -1 -1
+2.42383258110959 0.666237598132384 -1 -1
+0.747348132067155 1.47264064251741 -1 -1
+0.838865215496192 1.76783636185541 -1 -1
+4.52610068296141 1.46232793550644 -1 -1
+1.06513040906981 2.40015863837706 -1 -1
+2.1234030850167 1.67672406529316 -1 -1
+3.93362700547878 1.77953431787149 -1 -1
+5.240508093196 3.19583522613743 -1 -1

w3/w3.Rproj

@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX