# Custom Density Functions

To sample from a custom density function, create said function, then use it as the `custom_density` parameter with one of the samplers, instead of giving `distr_name`, `distr_params` (see Supported Distributions and How to Sample) and `weights`, if applicable.

In this example we create a 2D uniform distribution, which is not supported by Rcpp and RcppDist at the time of writing.

    # Create function
    customDensity_r <- function(x){
      if (x[1] > 0 && x[1] < 3 && x[2] < 0 && x[2] > -3){
        return (1)
      } else {
        return (0)                    }
    }
    # Sample
    Y <- samplr::sampler_mh(start = c(0,0), custom_density = customDensity_r)
    #> Warning in .checkSigmaProp(sigma_prop, start_dim): The variance of the proposal
    #> distribution was not given and defaulted to the identity matrix

    # Plot results
    x <- Y[[1]][,1]
    y <- Y[[1]][,2]
    plot(x,y, xlim = c(-5,5), ylim = c(-5,5))

#### On Timing

Using a custom function, rather than sampling from a supported distribution, will considerably increase the running time (as the C++ code under the hood has to constantly switch between C++ and R). Creating a density function with `Rcpp::cppFunction` doesn’t change this much (it’s actually probably worse).

    Rcpp::cppFunction("double customDensity_cpp(NumericVector x){
                        if (x(0) > 0 && x(0) < 3 && x(1) < 0 && x(1) > -3){
                          return 1;
                        } else {
                          return 0;
                        }
                      }")
    X <- bench::mark(
      "CPP Density" = {
        samplr::sampler_mh(
          start = c(0,0),
          custom_density = customDensity_cpp,
          sigma_prop = diag(2))
      },
      "R Density" = {
        samplr::sampler_mh(
          start = c(0,0),
          custom_density = customDensity_r,
          sigma_prop = diag(2))
      },
      "Supported Density" = {
        samplr::sampler_mh(
          start = c(0,0),
          distr_name = "mvnorm",
          distr_params = list(c(0,1), diag(2)),
          sigma_prop = diag(2))
      },
      check = FALSE,
    )
    knitr::kable(as.data.frame(X[,c("expression", "min", "median")]))

expression | min | median
--- | --- | ---
CPP Density | 3.03ms | 4.14ms
R Density | 2.38ms | 2.65ms
Supported Density | 1.01ms | 1.5ms

Note, however, than this is still much faster than doing everything in R.

    bespoke_mh <- function(pdf, start, iterations=1024, sigma_prop){
      # Initialize variables ---------------------------------
      acceptances <- 0
      n_dim <- length(start)
      chain <- matrix(0, nrow=iterations, ncol = n_dim)
      chain[1,] <- start
      probabilities <- c(pdf(start))

      # Run the sampler ------------------------------------------------

      for (i in 2:iterations){
        current_x <- chain[i-1,]
        # generate proposal
        proposal <- mvtnorm::rmvnorm(1, mean = current_x, sigma = sigma_prop)
        # calculate current and proposal probabilities
        prob_curr <- probabilities[i-1]
        prob_prop <- pdf(proposal)
        accept <- FALSE

        # proposal is accepted with probability prob_prop / prob_curr
        if (prob_curr != 0){
          ratio <- prob_prop / prob_curr
          if (ratio >= 1){
            accept <- TRUE
          # The beta parameter (temperature), beta <= 1,
          # increases the value of the ratio making hotter
          # chains more likely to accept proposals
          } else if (stats::runif(1) < ratio){
            accept <- TRUE
          }
        } else {
        # in case the current probability is 0
        # (in which case the ratio cannot be calculated),
        # the step is accepted if the probability of the proposal is not 0
            if (prob_prop > 0) {
              accept <- TRUE
            }
        }

        if (accept){
          chain[i,] <- proposal
          acceptances <- acceptances +  1
          probabilities[i] <- prob_prop
        } else {
          chain[i,] <- current_x
          probabilities[i] <- prob_curr
        }

      }

      return(list(
        chain = chain,
        acceptance_ratio = acceptances/(iterations - 1)))
    }

    X <- bench::mark(
      "Bespoke MH" = {
        bespoke_mh(
          customDensity_r,
          c(0,0),
          iterations = 1024,
          sigma_prop = diag(2)
        )
      }
    )
    #> Warning: Some expressions had a GC in every iteration; so filtering is
    #> disabled.
    knitr::kable(as.data.frame(X[,c("expression", "min", "median")]))

expression | min | median
--- | --- | ---
Bespoke MH | 158ms | 170ms