56 lines
1.7 KiB
Rust
56 lines
1.7 KiB
Rust
extern crate blas_src;
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use kickscore::{kernel, model::Binary};
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fn main() {
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let mut model = Binary::probit();
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// Spike's skill does not change over time.
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let k_spike = kernel::Constant::new(0.5);
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// Tom's skill changes over time, with "jagged" (non-smooth) dynamics.
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let k_tom = kernel::Exponential::new(1.0, 1.0);
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// Jerry's skill has a constant offset and smooth dynamics.
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let k_jerry = (kernel::Constant::new(1.0), kernel::Matern52::new(0.5, 1.0));
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// Now we are ready to add the items in the model.
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model.add_item("Spike", k_spike);
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model.add_item("Tom", k_tom);
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model.add_item("Jerry", k_jerry);
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// At first, Jerry beats Tom a couple of times.
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model.observe(&["Jerry"], &["Tom"], 0.0);
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model.observe(&["Jerry"], &["Tom"], 0.9);
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// Then, Tom beats Spike, and then Jerry.
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model.observe(&["Tom"], &["Spike"], 1.7);
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model.observe(&["Tom"], &["Jerry"], 2.1);
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// Finally, Jerry beats Tom, and then Tom + Spike.
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model.observe(&["Jerry"], &["Tom"], 3.0);
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model.observe(&["Jerry"], &["Tom", "Spike"], 3.5);
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model.fit();
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// We can predict a future outcome...
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let (p_win, _p_los) = model.probabilities(&["Jerry"], &["Tom"], 4.0);
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println!(
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"Chances that Jerry beats Tom at t = 4.0: {:.1}%",
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100.0 * p_win
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);
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// ... or simulate what could have happened in the past.
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let (p_win, _p_los) = model.probabilities(&["Jerry"], &["Tom"], 2.0);
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println!(
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"Chances that Jerry beats Tom at t = 2.0: {:.1}%",
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100.0 * p_win
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);
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let (p_win, _p_los) = model.probabilities(&["Jerry"], &["Tom"], -1.0);
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println!(
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"Chances that Jerry beats Tom at t = -1.0: {:.1}%",
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100.0 * p_win
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);
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}
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