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//use crate::common::validate_inputs;
use crate::indicators::simd_indicators::road_train::{Asset, Driver, PrimeMover};
use crate::indicators::simd_indicators::wilders_simd::{calc_simd, init_state};
use crate::indicators::wilders::{
min_data, multiplier, output_length, IndicatorState, INPUTS_WIDTH, OPTIONS_WIDTH,
};
use crate::types::IndicatorError;
use crate::{common::validate_options, common_simd::assets::validate_inputs};
use std::simd::Simd;
/// SIMD driver that advances Wilder's Smoothing (WILDERS) across `N` asset lanes per scheduling epoch.
struct WildersDriver {
multipliers: (f64, f64),
}
impl Driver<f64> for WildersDriver {
/// Processes one epoch of bars for `N` assets simultaneously using SIMD.
fn next_run<const N: usize>(
&mut self,
inputs: Vec<Vec<&[f64]>>,
mut outputs: Vec<Vec<&mut [f64]>>,
mut states: Vec<&mut f64>,
_options: Vec<Option<&()>>,
) {
let len = inputs[0][0].len();
// Optimization 1: Direct array construction instead of collect+try_into
let mut wilders = Simd::<f64, N>::from_array(std::array::from_fn(|i| unsafe {
**states.get_unchecked(i)
}));
let multipliers = (
Simd::splat(self.multipliers.0),
Simd::splat(self.multipliers.1),
);
// Optimization 2: Pre-compute all input and output pointers
let input_ptrs = crate::extract_input_ptrs!(inputs, N, real_ptrs);
let output_ptrs = crate::extract_output_ptrs!(outputs, N, sma_line_ptr);
// Optimization 3: Simplified main loop with pre-computed offsets
for i in 0..len {
let real = crate::extract_simd_at_indices!(N, input_ptrs,
real @ i
);
wilders = calc_simd(wilders, real, multipliers);
crate::write_simd_at_indices!(N, i,
output_ptrs => wilders
);
}
// Update states efficiently
let final_wilders = wilders.to_array();
for (i, state) in states.iter_mut().enumerate().take(N) {
**state = final_wilders[i];
}
}
}
/// Calculates Wilder's Smoothing (WILDERS) for `N` assets simultaneously using SIMD parallelism.
///
/// WILDERS produces no optional outputs. Uses the [`PrimeMover`] scheduler to batch assets into
/// SIMD-width groups.
///
/// # Arguments
/// * `inputs` - An array of `N` asset input sets; `inputs[i]` is `[&[f64]; INPUTS_WIDTH]`
/// containing `[real]` for asset `i`.
/// * `options` - `options[0]` is the `period`.
/// * `_optional_outputs` - Unused; WILDERS has no optional outputs.
///
/// # Returns
/// `Ok((outputs, states))` where `outputs[i][0]` is the Wilder's Smoothing line for asset `i` and
/// `states[i]` is the final [`IndicatorState`] for asset `i`.
/// Returns `Err(IndicatorError)` if any input slice is too short.
pub fn indicator_by_assets<const N: usize>(
inputs: &[&[&[f64]; INPUTS_WIDTH]; N], //stock[ fields [ field [f64] ] ]
options: &[f64; OPTIONS_WIDTH],
_optional_outputs: Option<&[bool]>,
) -> Result<(Vec<Vec<Vec<f64>>>, Vec<IndicatorState>), IndicatorError> {
validate_inputs::<INPUTS_WIDTH>(inputs, min_data(options))?;
validate_options(options)?;
let period = options[0] as usize;
//let real: Vec<&[f64]> = (0..N).map(|i| inputs[i][0]).collect();
let real: [&[f64]; N] = std::array::from_fn(|i| inputs[i][0]);
//init ema, sliced inputs and multipliers
let wilders = init_state(&real, period).to_array();
let multipliers = multiplier(period);
let mut road_train = PrimeMover::<N, f64>::new();
let mut output_buffers: Vec<Vec<Vec<f64>>> = (0..N)
.map(|i| {
vec![{
let capacity = output_length(inputs[i][0].len(), options);
crate::uninit_vec!(f64, capacity)
}]
})
.collect();
for i in 0..N {
let asset_inputs = vec![inputs[i][0]];
unsafe {
// Get a mutable reference to the output buffer for this asset
let output_buffer = &mut output_buffers[i][0];
let asset_outputs = vec![std::slice::from_raw_parts_mut(
output_buffer.as_mut_ptr(),
output_buffer.len(),
)];
road_train.add_asset(Asset::new(
asset_inputs,
asset_outputs,
i,
period,
0,
wilders[i],
None,
));
}
}
let mut driver = WildersDriver { multipliers };
let wilders = road_train.drive(&mut driver);
let mut states = Vec::with_capacity(N);
for &wilder in wilders.iter() {
states.push(IndicatorState::new(wilder, multipliers));
}
Ok((output_buffers, states))
}
/*pub fn indicator_by_assets_from_state<const N: usize>(
inputs: &[ &[ &[f64]; INPUTS_WIDTH]; N],
states: &mut [IndicatorState; N],
_optional_outputs: Option<&[bool]>,
) -> Result<[Vec<Vec<f64>>; N], IndicatorError>
{
let len = inputs[0][0].len();
// Validate all inputs have same length
for i in 0..N {
if inputs[i][0].len() != len {
return Err(IndicatorError::InvalidInputs);
}
}
// Extract EMAs and multipliers from states
let mut emas = Simd::from_array(std::array::from_fn(|i| states[i].get_ema()));
let multipliers = states[0].get_multipliers();
let multipliers_simd = (Simd::splat(multipliers.0), Simd::splat(multipliers.1));
// Create output arrays and process directly
let mut ema_lines: [Vec<Vec<f64>>; N] = std::array::from_fn(|_| {
vec![crate::uninit_vec!(f64, len)]
});
for i in 0..len {
//let values: [f64; N] = (0..N).map(|j| inputs[j][0][i]).collect::<Vec<_>>().try_into().unwrap();
let values: [f64; N] = std::array::from_fn(|j| inputs[j][0][i]);
let vals = Simd::from_array(values);
emas = calc_simd(vals, emas, multipliers_simd);
let outputs = emas.to_array();
for j in 0..N {
unsafe { *ema_lines[j].get_unchecked_mut(0).get_unchecked_mut(i) = outputs[j] }
}
}
// Update states with final EMA values
let final_emas = emas.to_array();
for i in 0..N {
states[i].set_ema(final_emas[i]);
}
Ok(ema_lines)
}*/