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use std::sync::Arc;
use fieldx::fxstruct;
use rand_distr::Distribution;
use rand_distr::SkewNormal;
use crate::test::simulation::db::entity::Product as DbProduct;
use crate::test::simulation::scriptwriter::model::product::ProductModel;
#[derive(Clone, Debug)]
#[fxstruct(sync, no_new, builder, get(copy))]
pub struct Product {
/// Unique product ID
id: i32,
/// Product name
#[fieldx(get(copy(off)))]
name: String,
/// Product price
price: f64,
/// The expected daily customer interest for the product, expressed as a fraction of 1.0 where 1.0 means every
/// customer wants it. This metric quantifies the product's popularity. Note that the product's price also
/// influences final sales.
daily_quotient: f64,
/// Expected return rate of the product, %/period.
expected_return_rate: f64,
/// Expected terms of shipment arrival from the supplier, in days. The final value for each shipment is sampled
/// using SkewedNormal distribution with the parameters derived from the following three values. stock_supplies_in
/// is the mean or location parameter.
stock_supplies_in: f64,
/// Specifies the variability of the shipment terms. The value is a percentage of the stock_supplies_in value. The
/// higher the value, the more uncertain the shipment terms are; values above 20% are not accepted. This value
/// translates into the SkewNormal distribution scale parameter by the formula:
/// scale = supplier_inaccuracy * stock_supplies_in / 1.6448536
supplier_inaccuracy: f64,
/// Specifies the tendency of a supplier to delay the shipment. Positive values indicate a tendency to delay and
/// measured as a percentage of the late shipments. Negative values indicate a tendency to deliver faster. Reasonable
/// range for the value is between -0.9 and 0.7. Whereas the former is simply optimistic, the latter is rather
/// the maximum reasonably acceptable in real life.
/// The value translates into SkewedNormal distribution shape parameter by the formula:
/// shape = tan(PI * (supplier_tardiness - 0.5))
supplier_tardiness: f64,
#[fieldx(get(clone), serde(off))]
product_model: Arc<ProductModel>,
#[fieldx(lazy, private, get(copy(off)), builder(off), serde(off))]
supply_distribution: SkewNormal<f64>,
/// The expected number of items of this product sold to a single customer per day. This value is used in the
/// sampling process to simulate how many items of this product a customer buys in a single order.
#[fieldx(lazy, get(copy), builder(off))]
daily_estimate: f64,
/// How likely the product is to be viewed by a customer when purchase decision is being made.
#[fieldx(lazy, get(copy))]
view_probability: f64,
}
impl Product {
fn build_supply_distribution(&self) -> SkewNormal<f64> {
self.product_model()
.supply_distribution(
self.stock_supplies_in(),
self.supplier_inaccuracy(),
self.supplier_tardiness(),
)
.unwrap()
}
fn build_daily_estimate(&self) -> f64 {
self.product_model().customer_interest(self.price()) * self.daily_quotient()
}
fn build_view_probability(&self) -> f64 {
// Here we try to simulate a psychological effect of the product price on the customer. The higher the price,
// the more likely the customer to view it; and vice versa. In the latter case they would rather buy something
// cheap by immediately sending a product directly to the cart, without entering its page. Contrary, they're
// less likely to buy a more expensive product but would still willing to look at it.
// The formula simulates the effect by squeezing the customer interest curve towards its center by 30%.
((self.product_model().customer_interest(self.price()) - 0.5) * 0.85 + 0.5) * self.daily_quotient()
}
pub fn supplies_in(&self) -> f64 {
self.supply_distribution().sample(&mut rand::rng())
}
}
impl From<Product> for DbProduct {
fn from(product: Product) -> Self {
DbProduct {
id: product.id,
name: product.name,
price: product.price,
views: 0,
}
}
}