could anyone help me how to run the python script in matlab

python script:
## Generate all partitions to solve k-means anticlustering
## to optimality.

N <- 14
K <- 2
features <- matrix(sample(N * 2, replace = TRUE), ncol = 2)
partitions <- generate_partitions(K, N)
length(partitions) # number of possible partitions

## Create an objective function that takes the partition
## as first argument (then, we can use sapply to compute
## the objective for each partition)
var_obj <- function(clusters, features) {
  variance_objective(features, clusters)
}

all_objectives <- sapply(
  partitions,
  FUN = var_obj,
  features = features
)

## Check out distribution of the objective over all partitions:
hist(all_objectives) # many large, few low objectives
## Get best k-means anticlustering objective:
best_obj <- max(all_objectives)
## It is possible that there are multiple best solutions:
sum(all_objectives == best_obj)
## Select one best partition:
best_anticlustering <- partitions[all_objectives == best_obj][[1]]
## Look at mean for each partition:
by(features, best_anticlustering, function(x) round(colMeans(x), 2))


## Get best k-means clustering objective:
min_obj <- min(all_objectives)
sum(all_objectives == min_obj)
## Select one best partition:
best_clustering <- partitions[all_objectives == min_obj][[1]]

## Plot minimum and maximum objectives:
par(mfrow = c(1, 2))
plot_clusters(
  features,
  best_anticlustering,
  illustrate_variance = TRUE,
  main = "Maximum variance"
)
plot_clusters(
  features,
  best_clustering,
  illustrate_variance = TRUE,
  main = "Minimum variance"
)
par(mfrow = c(1, 1))

could anyone please help me on this.

[Rik]

Do you just want to run this, or is there an error in the python code?