diff --git a/R/getDistances.R b/R/getDistances.R
new file mode 100644
index 0000000..853be3b
--- /dev/null
+++ b/R/getDistances.R
@@ -0,0 +1,43 @@
+getDistances <- function(data_matrix, nclusters) {
+
+	# %finds initial admixture clustering solution with nclusters clusters, uses simple mean Hamming distance
+	# %gives partition in 8 - bit format
+	# %allocates all alleles of a single individual into the same basket
+	# %data_matrix contains #Loci + 1 columns, last column indicate whose alleles are placed in each row,
+	# %i.e. ranges from 1 to #individuals. For diploids there are 2 rows per individual, for haploids only a single row
+	# %missing values are indicated by zeros in the partition and by negative integers in the data_matrix.
+
+	size_data <- size(data_matrix)
+	nloci <- size_data[2] - 1
+	n <- max(data_matrix[, ncol(data_matrix)])
+	distances <- zeros(choose(n, 2), 1)
+	pointer <- 1
+	for (i in 1:n - 1) {
+		i_data <- data_matrix[
+			find(data_matrix[, ncol(data_matrix)] == i),
+			1:nloci
+		]
+		for (j in (i + 1):n) {
+			d_ij <- 0
+			j_data <- data_matrix[find(data_matrix[, ncol()] == j), 1:nloci]
+			vertailuja <- 0
+			for (k in 1:size(i_data, 1)) {
+				for (l in 1:size(j_data, 1)) {
+					here_i <- find(i_data[k, ] >= 0)
+					here_j <- find(j_data[l, ] >= 0)
+					here_joint <- intersect(here_i, here_j)
+					vertailuja <- vertailuja + length(here_joint)
+					d_ij <- d_ij + length(
+						find(i_data[k, here_joint] != j_data[l, here_joint])
+					)
+				}
+			}
+			d_ij <- d_ij / vertailuja
+			distances[pointer] <- d_ij
+			pointer <- pointer + 1
+		}
+	}
+
+	Z <- linkage(t(distances))
+	return(list(Z = Z, distances = distances))
+}