When trying to train a model with a dataset of around 3 million rows and 600 columns using the C5.0 CRAN package I get the following error:
Error in paste(apply(x, 1, paste, collapse = ","), collapse = "\n") : result would exceed 2^31-1 bytes
From what the owner of the repository answered to a similar issue, it is due to an R limitation in the number of bytes in a character string, which is limited to 2^31 - 1.
Long answer ahead:
So, as stated in the question, the error occurs in the last line of the makeDataFile function from the Cubist package, used in C5.0, which concatenates all rows into one string. As this string is needed to pass the data to the C5.0 function in C, but is not needed to make any operations in R, and C has no memory limitation aside from those of the machine itself, the approach I have taken is to create such string in C instead. In order to do this, the R code will pass the information in a character vector containing various strings that don’t surpass the length limit, instead of one, so that once in C these elements can be concatenated.
However, instead of leaving all rows as separate elements in the character vector to be concatenated in C using strcat in a loop, I have found that the strcat function is quite slow, so I have chosen to create another R function (create_max_len_strings) in order to concatenate the rows into the longest (~or close~) strings possible without reaching the memory limit so that strcat only needs to be applied a few times to concatenate these longer strings.
So, the last line of the original makeDataFile() function will be replaced so that each row is left separately as an element of a character vector, only adding a line break at the end of each string row so that when concatenating some of these elements into longer strings, using create_max_len_strings(), they will be differentiated:
makeDataFile.R:
create_max_len_strings <- function(original_vector) {
vector_length = length(original_vector)
nchars = sum(nchar(original_vector, type = "chars"))
## Check if the length of the string would reach 1900000000, which is close to the memory limitation
if(nchars >= 1900000000){
## Calculate how many strings we could create of the maximum length
nchunks = 0
while(nchars > 0){
nchars = nchars - 1900000000
nchunks = nchunks + 1
}
## Get the number of rows that would be contained in each string
chunk_size = vector_length/nchunks
## Get the rounded number of rows in each string
chunk_size = floor(chunk_size)
index = chunk_size
## Create a vector with the indexes of the rows that delimit each string
indexes_vector = c()
indexes_vector = append(indexes_vector, 0)
n = nchunks
while(n > 0){
indexes_vector = append(indexes_vector, index)
index = index + chunk_size
n = n - 1
}
## Get the last few rows if the division had remainder
remainder = vector_length %% nchunks
if (remainder != 0){
indexes_vector = append(indexes_vector, vector_length)
nchunks = nchunks + 1
}
## Create the strings pasting together the rows from the indexes in the indexes vector
strings_vector = c()
i = 2
while (i <= length(indexes_vector)){
## Sum 1 to the index_init so that the next string does not contain the last row of the previous string
index_init = indexes_vector[i-1] + 1
index_end = indexes_vector[i]
## Paste the rows from the vector from index_init to index_end
string <- paste0(original_vector[index_init:index_end], collapse="")
## Create vector containing the strings that were created
strings_vector <- append(strings_vector, string)
i = i + 1
}
}else {
strings_vector = paste0(original_vector, collapse="")
}
strings_vector
}
makeDataFile <- function(x, y, w = NULL) {
## Previous code stays the same
...
x = apply(x, 1, paste, collapse = ",")
x = paste(x, "\n", sep="")
char_vec = create_max_len_strings(x)
}
CALLING C5.0
Now, in order to create the final string to pass to the c50() function in C, an intermediate function is created and called instead. In order to do this, the .C() statement that calls c50() in R is replaced with a .Call() statement calling this function, as .Call() allows for complex objects such as vectors to be passed to C. Also, it allows for the result to be returned in the variable result instead of having to pass back the variables tree, rules and output by reference. The result of calling C5.0 will be received in the character vector result containing the strings corresponding to the tree, rules and output in the first three positions:
C5.0.R:
C5.0.default <- function(x,
y,
trials = 1,
rules = FALSE,
weights = NULL,
control = C5.0Control(),
costs = NULL,
...) {
## Previous code stays the same
...
dataString <- makeDataFile(x, y, weights)
num_chars = sum(nchar(dataString, type = "chars"))
result <- .Call(
"call_C50",
as.character(namesString),
dataString,
as.character(num_chars), ## The length of the resulting string is passed as character because it is too long for an integer
as.character(costString),
as.logical(control$subset),
# -s "use the Subset option" var name: SUBSET
as.logical(rules),
# -r "use the Ruleset option" var name: RULES
## for the bands option, I'm not sure what the default should be.
as.integer(control$bands),
# -u "sort rules by their utility into bands" var name: UTILITY
## The documentation has two options for boosting:
## -b use the Boosting option with 10 trials
## -t trials ditto with specified number of trial
## I think we should use -t
as.integer(trials),
# -t : " ditto with specified number of trial", var name: TRIALS
as.logical(control$winnow),
# -w "winnow attributes before constructing a classifier" var name: WINNOW
as.double(control$sample),
# -S : use a sample of x% for training
# and a disjoint sample for testing var name: SAMPLE
as.integer(control$seed),
# -I : set the sampling seed value
as.integer(control$noGlobalPruning),
# -g: "turn off the global tree pruning stage" var name: GLOBAL
as.double(control$CF),
# -c: "set the Pruning CF value" var name: CF
## Also, for the number of minimum cases, I'm not sure what the
## default should be. The code looks like it dynamically sets the
## value (as opposed to a static, universal integer
as.integer(control$minCases),
# -m : "set the Minimum cases" var name: MINITEMS
as.logical(control$fuzzyThreshold),
# -p "use the Fuzzy thresholds option" var name: PROBTHRESH
as.logical(control$earlyStopping)
)
## Get the first three positions of the character vector that contain the tree, rules and output returned by C5.0 in C
result_tree = result[1]
result_rules = result[2]
result_output = result[3]
modelContent <- strsplit(
if (rules)
result_rules
else
result_tree, "\n"
)[[1]]
entries <- grep("^entries", modelContent, value = TRUE)
if (length(entries) > 0) {
actual <- as.numeric(substring(entries, 10, nchar(entries) - 1))
} else
actual <- trials
if (trials > 1) {
boostResults <- getBoostResults(result_output)
## This next line is here to avoid a false positive warning in R
## CMD check:
## * checking R code for possible problems ... NOTE
## C5.0.default: no visible binding for global variable 'Data'
Data <- NULL
size <-
if (!is.null(boostResults))
subset(boostResults, Data == "Training Set")$Size
else
NA
} else {
boostResults <- NULL
size <- length(grep("[0-9])$", strsplit(result_output, "\n")[[1]]))
}
out <- list(
names = namesString,
cost = costString,
costMatrix = costs,
caseWeights = !is.null(weights),
control = control,
trials = c(Requested = trials, Actual = actual),
rbm = rules,
boostResults = boostResults,
size = size,
dims = dim(x),
call = funcCall,
levels = levels(y),
output = result_output,
tree = result_tree,
predictors = colnames(x),
rules = result_rules
)
class(out) <- "C5.0"
out
}
Now onto the C code, the function call_c50() basically acts as an intermediate between the R code and the C code, concatenating the elements in the dataString array to obtain the string needed by the C function c50(), by accessing each position of the array using CHAR(STRING_ELT(x, i)) and concatenating (strcat) them together. Then the rest of the variables are casted to their respective types and the c50() function in file top.c (where this function should also be placed) is called. The result of calling c50() will be returned to the R routine by creating a character vector and placing the strings corresponding to the tree, rules and output in each position.
Lastly, the c50() function is basically left as is, except for the variables treev, rulesv and outputv, as these are the values that are going to be returned by .Call() instead of being passed by reference, they no longer need to be in the arguments of the function. As they are all strings they can be returned in a single array, by setting each string to a position in the array c50_return.
top.c:
SEXP call_C50(SEXP namesString, SEXP data_vec, SEXP datavec_len, SEXP costString, SEXP subset, SEXP rules, SEXP bands, SEXP trials, SEXP winnow, SEXP sample,
SEXP seed, SEXP noGlobalPruning, SEXP CF, SEXP minCases, SEXP fuzzyThreshold, SEXP earlyStopping){
char* string;
char* concat;
long n = 0;
long size;
int i;
char* eptr;
// Get the length of the data vector
n = length(data_vec);
// Get the string indicating the length of the final string
char* size_str = malloc((strlen(CHAR(STRING_ELT(datavec_len, 0)))+1)*sizeof(char));
strcpy(size_str, CHAR(STRING_ELT(datavec_len, 0)));
// Turn the string to long
size = strtol(size_str, &eptr, 10);
// Allocate memory for the number of characters indicated by datavec_len
string = malloc((size+1)*sizeof(char));
// Copy the first element of data_vec into the string variable
strcpy(string, CHAR(STRING_ELT(data_vec, 0)));
// Loop over the data vector until all elements are concatenated in the string variable
for (i = 1; i < n; i++) {
strcat(string, CHAR(STRING_ELT(data_vec, i)));
}
// Copy the value of namesString into a char*
char* namesv = malloc((strlen(CHAR(STRING_ELT(namesString, 0)))+1)*sizeof(char));
strcpy(namesv, CHAR(STRING_ELT(namesString, 0)));
// Copy the value of costString into a char*
char* costv = malloc((strlen(CHAR(STRING_ELT(costString, 0)))+1)*sizeof(char));
strcpy(costv, CHAR(STRING_ELT(costString, 0)));
// Call c50() function casting the rest of arguments into their respective C types
char** c50_return = c50(namesv, string, costv, asLogical(subset), asLogical(rules), asInteger(bands), asInteger(trials), asLogical(winnow), asReal(sample), asInteger(seed), asInteger(noGlobalPruning), asReal(CF), asInteger(minCases), asLogical(fuzzyThreshold), asLogical(earlyStopping));
free(string);
free(namesv);
free(costv);
// Create a character vector to be returned to the C5.0 R function
SEXP out = PROTECT(allocVector(STRSXP, 3));
SET_STRING_ELT(out, 0, mkChar(c50_return[0]));
SET_STRING_ELT(out, 1, mkChar(c50_return[1]));
SET_STRING_ELT(out, 2, mkChar(c50_return[2]));
UNPROTECT(1);
return out;
}
static char** c50(char *namesv, char *datav, char *costv, int subset,
int rules, int utility, int trials, int winnow,
double sample, int seed, int noGlobalPruning, double CF,
int minCases, int fuzzyThreshold, int earlyStopping) {
int val; /* Used by setjmp/longjmp for implementing rbm_exit */
char ** c50_return = malloc(3 * sizeof(char*));
// Initialize the globals to the values that the c50
// program would have at the start of execution
initglobals();
// Set globals based on the arguments. This is analogous
// to parsing the command line in the c50 program.
setglobals(subset, rules, utility, trials, winnow, sample, seed,
noGlobalPruning, CF, minCases, fuzzyThreshold, earlyStopping,
costv);
// Handles the strbufv data structure
rbm_removeall();
// Deallocates memory allocated by NewCase.
// Not necessary since it's also called at the end of this function,
// but it doesn't hurt, and I'm feeling paranoid.
FreeCases();
// XXX Should this be controlled via an option?
// Rprintf("Calling setOf\n");
setOf();
// Create a strbuf using *namesv as the buffer.
// Note that this is a readonly strbuf since we can't
// extend *namesv.
STRBUF *sb_names = strbuf_create_full(namesv, strlen(namesv))
// Register this strbuf using the name "undefined.names"
if (rbm_register(sb_names, "undefined.names", 0) < 0) {
error("undefined.names already exists");
}
// Create a strbuf using *datav and register it as "undefined.data"
STRBUF *sb_datav = strbuf_create_full(datav, strlen(datav));
// XXX why is sb_datav copied? was that part of my debugging?
// XXX or is this the cause of the leak?
if (rbm_register(strbuf_copy(sb_datav), "undefined.data", 0) < 0) {
error("undefined data already exists");
}
// Create a strbuf using *costv and register it as "undefined.costs"
if (strlen(costv) > 0) {
// Rprintf("registering cost matrix: %s", *costv);
STRBUF *sb_costv = strbuf_create_full(costv, strlen(costv));
// XXX should sb_costv be copied?
if (rbm_register(sb_costv, "undefined.costs", 0) < 0) {
error("undefined.cost already exists");
}
} else {
// Rprintf("no cost matrix to register\n");
}
/*
* We need to initialize rbm_buf before calling any code that
* might call exit/rbm_exit.
*/
if ((val = setjmp(rbm_buf)) == 0) {
// Real work is done here
c50main();
if (rules == 0) {
// Get the contents of the the tree file
STRBUF *treebuf = rbm_lookup("undefined.tree");
if (treebuf != NULL) {
char *treeString = strbuf_getall(treebuf);
c50_return[0] = R_alloc(strlen(treeString) + 1, 1);
strcpy(c50_return[0], treeString);
c50_return[1] = "";
} else {
// XXX Should *treev be assigned something in this case?
// XXX Throw an error?
}
} else {
// Get the contents of the the rules file
STRBUF *rulesbuf = rbm_lookup("undefined.rules");
if (rulesbuf != NULL) {
char *rulesString = strbuf_getall(rulesbuf);
c50_return[1] = R_alloc(strlen(rulesString) + 1, 1);
strcpy(c50_return[1], rulesString);
c50_return[0] = "";
} else {
// XXX Should *rulesv be assigned something in this case?
// XXX Throw an error?
}
}
} else {
Rprintf("c50 code called exit with value %d\n", val - JMP_OFFSET);
}
// Close file object "Of", and return its contents via argument outputv
char *outputString = closeOf();
c50_return[2] = R_alloc(strlen(outputString) + 1, 1);
strcpy(c50_return[2], outputString);
// Deallocates memory allocated by NewCase
FreeCases();
// We reinitialize the globals on exit out of general paranoia
initglobals();
return c50_return;
}
***IMPORTANT: if the string created is longer than 2147483647, you also will need to change the definition of the variables i and j in the function strbuf_gets() in strbuf.c. This function basically iterates through each position of the string, so trying to increase their value above the INT limit to access those positions in the array will cause a segmentation fault. I suggest changing the declaration type to long in order to avoid this issue.
C5.0 PREDICTIONS
However, as the makeDataFile function is not only used to create the model but also to pass the data to the predictions() function, this function will also have to be modified. Just like previously, the .C() statement in predict.C5.0() used to call predictions() will be replaced with a .Call() statement in order to be able to pass the character vector to C, and the result will be returned in the result variable instead of being passed by reference:
predict.C5.0.R:
predict.C5.0 <- function (object,
newdata = NULL,
trials = object$trials["Actual"],
type = "class",
na.action = na.pass,
...) {
## Previous code stays the same
...
caseString <- makeDataFile(x = newdata, y = NULL)
num_chars = sum(nchar(caseString, type = "chars"))
## When passing trials to the C code, convert to
## zero if the original version of trials is used
if (trials <= 0)
stop("'trials should be a positive integer", call. = FALSE)
if (trials == object$trials["Actual"])
trials <- 0
## Add trials (not object$trials) as an argument
results <- .Call(
"call_predictions",
caseString,
as.character(num_chars),
as.character(object$names),
as.character(object$tree),
as.character(object$rules),
as.character(object$cost),
pred = integer(nrow(newdata)),
confidence = double(length(object$levels) * nrow(newdata)),
trials = as.integer(trials)
)
predictions = as.numeric(unlist(results[1]))
confidence = as.numeric(unlist(results[2]))
output = as.character(results[3])
if(any(grepl("Error limit exceeded", output)))
stop(output, call. = FALSE)
if (type == "class") {
out <- factor(object$levels[predictions], levels = object$levels)
} else {
out <-
matrix(confidence,
ncol = length(object$levels),
byrow = TRUE)
if (!is.null(rownames(newdata)))
rownames(out) <- rownames(newdata)
colnames(out) <- object$levels
}
out
}
In the file top.c, the predictions() function will be modified to receive the variables passed by the .Call() statement, so that just like previously, the caseString array will be concatenated into a single string and the rest of the variables casted to their respective types. In this case the variables pred and confidence will be also received as vectors of integer and double types and so they will need to be casted to int* and double*. The rest of the function is left as it was in order to create the predictions and the resulting variables predv, confidencev and output variables will be placed in the first three positions of a vector respectively.
top.c:
SEXP call_predictions(SEXP caseString, SEXP case_len, SEXP names, SEXP tree, SEXP rules, SEXP cost, SEXP pred, SEXP confidence, SEXP trials){
char* casev;
char* outputv = "";
char* eptr;
char* size_str = malloc((strlen(CHAR(STRING_ELT(case_len, 0)))+1)*sizeof(char));
strcpy(size_str, CHAR(STRING_ELT(case_len, 0)));
long size = strtol(size_str, &eptr, 10);
casev = malloc((size+1)*sizeof(char));
strcpy(casev, CHAR(STRING_ELT(caseString, 0)));
int n = length(caseString);
for (int i = 1; i < n; i++) {
strcat(casev, CHAR(STRING_ELT(caseString, i)));
}
char* namesv = malloc((strlen(CHAR(STRING_ELT(names, 0)))+1)*sizeof(char));
strcpy(namesv, CHAR(STRING_ELT(names, 0)));
char* treev = malloc((strlen(CHAR(STRING_ELT(tree, 0)))+1)*sizeof(char));
strcpy(treev, CHAR(STRING_ELT(tree, 0)));
char* rulesv = malloc((strlen(CHAR(STRING_ELT(rules, 0)))+1)*sizeof(char));
strcpy(rulesv, CHAR(STRING_ELT(rules, 0)));
char* costv = malloc((strlen(CHAR(STRING_ELT(cost, 0)))+1)*sizeof(char));
strcpy(costv, CHAR(STRING_ELT(cost, 0)));
int variable;
int* predv = &variable;
int npred = length(pred);
predv = malloc((npred+1)*sizeof(int));
for (int i = 0; i < npred; i++) {
predv[i] = INTEGER(pred)[i];
}
double variable1;
double* confidencev = &variable1;
int nconf = length(confidence);
confidencev = malloc((nconf+1)*sizeof(double));
for (int i = 0; i < nconf; i++) {
confidencev[i] = REAL(confidence)[i];
}
int* trialsv = &variable;
*trialsv = asInteger(trials);
/* Original code for predictions starts */
int val;
// Announce ourselves for testing
// Rprintf("predictions called\n");
// Initialize the globals
initglobals();
// Handles the strbufv data structure
rbm_removeall();
// XXX Should this be controlled via an option?
// Rprintf("Calling setOf\n");
setOf();
STRBUF *sb_cases = strbuf_create_full(casev, strlen(casev));
if (rbm_register(sb_cases, "undefined.cases", 0) < 0) {
error("undefined.cases already exists");
}
STRBUF *sb_names = strbuf_create_full(namesv, strlen(namesv));
if (rbm_register(sb_names, "undefined.names", 0) < 0) {
error("undefined.names already exists");
}
if (strlen(treev)) {
STRBUF *sb_treev = strbuf_create_full(treev, strlen(treev));
if (rbm_register(sb_treev, "undefined.tree", 0) < 0) {
error("undefined.tree already exists");
}
} else if (strlen(rulesv)) {
STRBUF *sb_rulesv = strbuf_create_full(rulesv, strlen(rulesv));
if (rbm_register(sb_rulesv, "undefined.rules", 0) < 0) {
error("undefined.rules already exists");
}
setrules(1);
} else {
error("either a tree or rules must be provided");
}
// Create a strbuf using *costv and register it as "undefined.costs"
if (strlen(costv) > 0) {
// Rprintf("registering cost matrix: %s", *costv);
STRBUF *sb_costv = strbuf_create_full(costv, strlen(costv));
// XXX should sb_costv be copied?
if (rbm_register(sb_costv, "undefined.costs", 0) < 0) {
error("undefined.cost already exists");
}
} else {
// Rprintf("no cost matrix to register\n");
}
if ((val = setjmp(rbm_buf)) == 0) {
// Real work is done here
// Rprintf("\n\nCalling rpredictmain\n");
rpredictmain(trialsv, predv, confidencev);
// Rprintf("predict finished\n\n");
} else {
// Rprintf("predict code called exit with value %d\n\n", val - JMP_OFFSET);
}
// Close file object "Of", and return its contents via argument outputv
char *outputString = closeOf();
char *output = R_alloc(strlen(outputString) + 1, 1);
strcpy(output, outputString);
// We reinitialize the globals on exit out of general paranoia
initglobals();
/* Original code for predictions ends */
free(namesv);
free(treev);
free(rulesv);
free(costv);
SEXP predx = PROTECT(allocVector(INTSXP, npred));
for (int i = 0; i < npred; i++) {
INTEGER(predx)[i] = predv[i];
}
SEXP confidencex = PROTECT(allocVector(REALSXP, nconf));
for (int i = 0; i < npred; i++) {
REAL(confidencex)[i] = confidencev[i];
}
SEXP outputx = PROTECT(allocVector(STRSXP, 1));
SET_STRING_ELT(outputx, 0, mkChar(output));
SEXP vector = PROTECT(allocVector(VECSXP, 3));
SET_VECTOR_ELT(vector, 0, predx);
SET_VECTOR_ELT(vector, 1, confidencex);
SET_VECTOR_ELT(vector, 2, outputx);
UNPROTECT(4);
free(predv);
free(confidencev);
return vector;
}
I was under the impression that the assignment to entry in nil map error would only happen if we would want to assign to a double map, that is, when a map on a deeper level is trying to be assigned while the higher one doesn't exist, e.g.:
var mm map[int]map[int]int
mm[1][2] = 3
But it also happens for a simple map (though with struct as a key):
package main
import "fmt"
type COO struct {
x int
y int
}
var neighbours map[COO][]COO
func main() {
for i := 0; i < 30; i++ {
for j := 0; j < 20; j++ {
var buds []COO
if i < 29 {
buds = append(buds, COO{x: i + 1, y: j})
}
if i > 0 {
buds = append(buds, COO{x: i - 1, y: j})
}
if j < 19 {
buds = append(buds, COO{x: i, y: j + 1})
}
if j > 0 {
buds = append(buds, COO{x: i, y: j - 1})
}
neighbours[COO{x: i, y: j}] = buds // <--- yields error
}
}
fmt.Println(neighbours)
}
What could be wrong?
You need to initialize neighbours: var neighbours = make(map[COO][]COO)
See the second section in: https://blog.golang.org/go-maps-in-action
You'll get a panic whenever you try to insert a value into a map that hasn't been initialized.
In Golang, everything is initialized to a zero value, it's the default value for uninitialized variables.
So, as it has been conceived, a map's zero value is nil. When trying to use an non-initialized map, it panics. (Kind of a null pointer exception)
Sometimes it can be useful, because if you know the zero value of something you don't have to initialize it explicitly:
var str string
str += "42"
fmt.Println(str)
// 42 ; A string zero value is ""
var i int
i++
fmt.Println(i)
// 1 ; An int zero value is 0
var b bool
b = !b
fmt.Println(b)
// true ; A bool zero value is false
If you have a Java background, that's the same thing: primitive types have a default value and objects are initialized to null;
Now, for more complex types like chan and map, the zero value is nil, that's why you have to use make to instantiate them. Pointers also have a nil zero value. The case of arrays and slice is a bit more tricky:
var a [2]int
fmt.Println(a)
// [0 0]
var b []int
fmt.Println(b)
// [] ; initialized to an empty slice
The compiler knows the length of the array (it cannot be changed) and its type, so it can already instantiate the right amount of memory. All of the values are initialized to their zero value (unlike C where you can have anything inside your array). For the slice, it is initialized to the empty slice [], so you can use append normally.
Now, for structs, it is the same as for arrays. Go creates a struct with all its fields initialized to zero values. It makes a deep initialization, example here:
type Point struct {
x int
y int
}
type Line struct {
a Point
b Point
}
func main() {
var line Line
// the %#v format prints Golang's deep representation of a value
fmt.Printf("%#v\n", line)
}
// main.Line{a:main.Point{x:0, y:0}, b:main.Point{x:0, y:0}}
Finally, the interface and func types are also initialized to nil.
That's really all there is to it. When working with complex types, you just have to remember to initialize them. The only exception is for arrays because you can't do make([2]int).
In your case, you have map of slice, so you need at least two steps to put something inside: Initialize the nested slice, and initialize the first map:
var buds []COO
neighbours := make(map[COO][]COO)
neighbours[COO{}] = buds
// alternative (shorter)
neighbours := make(map[COO][]COO)
// You have to use equal here because the type of neighbours[0] is known
neighbours[COO{}] = make([]COO, 0)
I can create a "static" map via
type m map[int]map[int]map[int]bool
but the length of "keys" will be dynamic:
|---unknown len--|
m[1][2][3][4][2][0] = true
or
|---unk len--|
m[1][2][3][4] = true
How I can create this map in Go? Or any way exists?
Added: Hierarchical is IMPORTANT
Thanks in advance!
The map type:
A map is an unordered group of elements of one type, called the element type, indexed by a set of unique keys of another type, called the key type.
A map type must have a specific value type and a specific key type. What you want does not qualify for this: you want a map where the value is sometimes another map (of the same type), and sometimes it's a bool.
Your options:
1. With a wrapper value type
The idea here is to not use just a simple (bool) value type, but a wrapper which holds both of your potential values: both a map and the simple value (bool):
type Value struct {
Children MapType
V bool
}
type MapType map[int]*Value
var m MapType
This is basically what user3591723 suggested, so I won't detail it further.
2. With a tree
This is a variant of #1, but this way we clearly communicate it's a tree.
The cleanest way to implement your hierarchical structure would be to use a tree, where a node could look like this:
type KeyType int
type ValueType string
type Node struct {
Children map[KeyType]*Node
Value ValueType
}
This has the advantage that you may choose the value type (which is bool in your case, but you can change it to whatever type - I used string for presentation).
For easily build / manage your tree, we can add some methods to our Node type:
func (n *Node) Add(key KeyType, v ValueType) {
if n.Children == nil {
n.Children = map[KeyType]*Node{}
}
n.Children[key] = &Node{Value: v}
}
func (n *Node) Get(keys ...KeyType) *Node {
for _, key := range keys {
n = n.Children[key]
}
return n
}
func (n *Node) Set(v ValueType, keys ...KeyType) {
n = n.Get(keys...)
n.Value = v
}
And using it: 1. build a tree, 2. query some values, 3. change a value:
root := &Node{Value: "root"}
root.Add(0, "first")
root.Get(0).Add(9, "second")
root.Get(0, 9).Add(3, "third")
root.Get(0).Add(4, "fourth")
fmt.Println(root)
fmt.Println(root.Get(0, 9, 3))
fmt.Println(root.Get(0, 4))
root.Set("fourthMod", 0, 4)
fmt.Println(root.Get(0, 4))
Output (try it on the Go Playground):
&{map[0:0x104382f0] root}
&{map[] third}
&{map[] fourth}
&{map[] fourthMod}
3. With a recursive type definition
It may be surprising but it is possible to define a map type in Go which has unlimited or dynamic "depth", using a recursive definition:
type X map[int]X
It is what it says: it's a map with int keys, and values of the same type as the map itself.
The big downside of this recursive type is that it can't store any "useful" data in the value type. It can only store the "fact" whether a value is present which is identical to a bool-like information (bool type: true or false), which may be enough in rare cases, but not in most.
Let's see an example building a "tree":
var x X
x = map[int]X{}
x[0] = map[int]X{}
x[0][9] = map[int]X{}
x[0][9][3] = map[int]X{}
x[0][4] = map[int]X{}
fmt.Println(x)
Output:
map[0:map[9:map[3:map[]] 4:map[]]]
If we want to test if there is a "value" based on a series of keys, we have 2 options: either use the special v, ok := m[i] indexing (which reports if a value for the specified key exists), or test if the value is not nil, e.g. m[i] != nil.
Let's see some examples testing the above built map:
var ok bool
_, ok = x[0][9][3]
fmt.Println("x[0][9][3] exists:", ok, "; alternative way:", x[0][9][3] != nil)
_, ok = x[0][9][4]
fmt.Println("x[0][9][4] exists:", ok, "; alternative way:", x[0][9][4] != nil)
_, ok = x[0][4]
fmt.Println("x[0][4] exists:", ok, "; alternative way:", x[0][4] != nil)
_, ok = x[0][4][9][9][9]
fmt.Println("x[0][4][9][9][9] exists:", ok, "; alternative way:", x[0][4][9][9][9] != nil)
Output:
x[0][9][3] exists: true ; alternative way: true
x[0][9][4] exists: false ; alternative way: false
x[0][4] exists: true ; alternative way: true
x[0][4][9][9][9] exists: false ; alternative way: false
Try these on the Go Playground.
Note: Even though x[0][4] is the last "leaf", indexing further like x[0][4][9][9][9] will not cause a panic as a nil map can be indexed and yields the zero value of the value type (which is nil in case the value type is a map type).
Ok I had some fun playing with this a bit. Here is a much better implementation than what I did before:
type mymap map[int]*myentry
type myentry struct {
m mymap
b bool
}
func (mm mymap) get(idx ...int) *myentry {
if len(idx) == 0 {
return nil
}
entry, ok := mm[idx[0]]
if !ok {
return nil
} else if len(idx) == 1 {
return entry
}
for i := 1; i < len(idx); i++ {
if entry == nil || entry.m == nil {
return nil
}
entry = entry.m[idx[i]]
}
return entry
}
func (mm mymap) setbool(v bool, idx ...int) {
if len(idx) == 0 {
return
}
if mm[idx[0]] == nil {
mm[idx[0]] = &myentry{m: make(mymap), b: false}
} else if mm[idx[0]].m == nil {
mm[idx[0]].m = make(mymap)
}
if len(idx) == 1 {
mm[idx[0]].b = v
return
}
entry := mm[idx[0]]
for i := 1; i < len(idx); i++ {
if entry.m == nil {
entry.m = make(mymap)
entry.m[idx[i]] = &myentry{m: make(mymap), b: false}
} else if entry.m[idx[i]] == nil {
entry.m[idx[i]] = &myentry{m: make(mymap), b: false}
}
entry = entry.m[idx[i]]
}
entry.b = v
}
func (m mymap) getbool(idx ...int) bool {
if val := m.get(idx...); val != nil {
return val.b
}
return false
}
func (m mymap) getmap(idx ...int) mymap {
if val := m.get(idx...); val != nil {
return val.m
}
return nil
}
Playground link
Something like that ought to get you started
If you don't need the hierarchical map structure and just want to use keys with variable length one approach could be to simply use strings as keys and one single map.
m := make(map[string]bool)
k := fmt.Sprintf("%v_%v_%v", 1, 2, 3)
m[k] = true
fmt.Println(m[k])
You cannot do this as this sort of type is not representable in Go's type system.
You will have to redesign.
E.g. a type arbitrarilyKeyedMapwith a method lookup(vals ...int) bool.
Probably you'll need methods for setting and deletion too.