Add construction for reciprocal allocation matrix
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# This file is automatically @generated by Cargo.
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# It is not intended for manual editing.
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version = 3
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name = "coster-rs"
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dependencies = [
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"nalgebra",
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]
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[[package]]
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name = "matrixmultiply"
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version = "0.3.2"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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name = "nalgebra"
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dependencies = [
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"approx",
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"matrixmultiply",
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"nalgebra-macros",
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"num-complex",
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"num-rational",
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"num-traits",
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"simba",
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"typenum",
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name = "nalgebra-macros"
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dependencies = [
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"proc-macro2",
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"quote",
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"syn",
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]
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[[package]]
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name = "num-complex"
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version = "0.4.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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name = "num-rational"
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dependencies = [
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"autocfg",
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name = "paste"
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[[package]]
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name = "proc-macro2"
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version = "1.0.39"
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name = "quote"
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dependencies = [
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"proc-macro2",
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]
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"bytemuck",
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[[package]]
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name = "simba"
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version = "0.7.1"
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dependencies = [
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"approx",
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"num-complex",
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"num-traits",
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"paste",
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"wide",
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]
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[[package]]
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name = "syn"
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version = "1.0.95"
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"proc-macro2",
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"quote",
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]
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[[package]]
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name = "typenum"
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name = "wide"
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dependencies = [
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"bytemuck",
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"safe_arch",
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9
Cargo.toml
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9
Cargo.toml
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[package]
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name = "coster-rs"
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version = "0.1.0"
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edition = "2021"
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# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
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[dependencies]
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nalgebra = "0.31.0"
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91
src/lib.rs
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extern crate nalgebra as na;
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use std::{collections::HashMap, ops::Mul};
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use na::DMatrix;
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// TODO: Could probably put this up a level by indicating how much of another department
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// each department used, then calculate the amounts from that.
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// Note: No need to include the operating departments, only service departments are needed,
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// then once we calculate all of the
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pub struct OverheadDepartmentAllocation {
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from_department: String,
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to_department: String,
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percent: f64,
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}
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pub struct TotalDepartmentCost {
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department: String,
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value: f64,
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}
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// Gets the matrix that can be used to reciprocally allocate line items in an account
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// TODO: What is actually supposed to be in the solve values? Not needed here but whatever calls this function will need to know this
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// Also need to handle errors (return appropriate result type)
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// TODO: Also need to return some order so we know what order ccs in the accounts should be in.. could just do this by returning a struct with
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// the matrix and a method to get the value for a particular key using the hashmap we created.
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fn get_reciprocal_allocation_matrix(allocations: Vec<OverheadDepartmentAllocation>, total_costs: Vec<TotalDepartmentCost>) -> DMatrix<f64> {
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// Convert vector to matrix form - matrix of from/to percent (usage) and vector of original costs
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// Matrix of all unique departments
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let mut department_mappings: HashMap<String, usize> = HashMap::new();
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for allocation in allocations.iter() {
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let map_size = department_mappings.len();
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department_mappings.entry(allocation.from_department.clone()).or_insert(map_size);
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let map_size = department_mappings.len();
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department_mappings.entry(allocation.to_department.clone()).or_insert(map_size);
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}
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let mut slice_allocations = vec![0.; department_mappings.len() * department_mappings.len()];
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// TODO: This needs to be passed in another time.
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let mut slice_costs = vec![0.; department_mappings.len()];
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for allocation in allocations {
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// TODO: Is there a more idiomatic way to do this?
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let elem = &mut slice_allocations[*department_mappings.get(&allocation.from_department).unwrap()];
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*elem = allocation.percent;
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}
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for cost in total_costs {
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let elem = &mut slice_costs[*department_mappings.get(&cost.department).unwrap()];
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*elem = cost.value;
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}
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// TODO: Would be nice to make this batched... matrix doesn't support that though.
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let mat: DMatrix<f64> = DMatrix::from_row_slice(department_mappings.len(), department_mappings.len(), &slice_allocations);
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let costs_vec: DMatrix<f64> = DMatrix::from_row_slice(department_mappings.len(), 1, &slice_costs);
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// Perform reciprocal allocation (LU solve or pseudoinverse regression if the matrix is singular - pseudo inverse is done using nalgebra svd)
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// TODO: Is it wasteful to perform the determinant rather than just immediately attempting lu? The implementation of determinant appears calls lu anyway?
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if mat.determinant() == 0. {
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// Pseudo inverse to find mininmum allocation
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// TODO: Error handling
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let pseudo_inverse = mat.svd(true, true).pseudo_inverse(0.000001);
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pseudo_inverse.unwrap().mul(&costs_vec)
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} else {
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// Standard solve using lu with partial pivoting.
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let lup = mat.lu();
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// TODO: Error handling
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lup.solve(&costs_vec).unwrap()
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}
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}
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// This is kind of a pointless function, it's just a matrix multiply... better to have a method that takes a function that can retrieve the accounts,
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// then an application would just need to pass in the batch retriever function and the initial overhead things.
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// Only issue that could come up with this is I get a case where I can't pass a function in from another language. Better the application itself just
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// uses the struct returned from the function above to
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fn allocate_overheads(allocation_matrix: DMatrix<f64>, ) {
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}
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// IDEA:
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// Consider a state-machine approach. Struct of allocations + total costs, then have a method to transform to
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// reciprocal matrix + hashmap of indexes, then another method that takes cc costs per account to transform into final outputs.
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// I think the state machine can be a higher-level api, and can make use of the above functions to transition between states.
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// This way you won't need to remember each step of the process, and it would be simpler to swap out implementations
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// as each struct in the state can swap out which functions it can use in the transition.
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3
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fn main() {
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println!("Hello, world!");
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}
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