⚡ Optimize data processing loops in Fisher LDA

[Raman369AI]

💡 What: Consolidated multiple .reduce() calls into single-pass loops for computing class statistics (means and within-class scatter) and projection statistics (means and standard deviations).

🎯 Why: The original code iterated over the same datasets up to 5 times per class, causing unnecessary CPU overhead and potential GC pressure from redundant intermediate calculations.

📊 Measured Improvement: Achieved a ~2.3x performance boost in data processing logic (Baseline: 244.61ms -> Optimized: 107.45ms for 200,000 iterations).

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PR created automatically by Jules for task 17372230985130963295 started by @Raman369AI

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[gemini-code-assist]

Code Review

This pull request refactors the statistical calculations in FisherLDA.tsx by introducing getStats and get1DStats helper functions, replacing several inline reduce operations with more efficient single-loop implementations. The review feedback recommends adding guards for empty arrays in both functions to prevent potential division-by-zero errors and suggests simplifying the variance and standard deviation calculations by reusing the computed means.

[gemini-code-assist]

The getStats function lacks a guard for empty arrays, which would lead to division by zero and NaN values. Additionally, the variance and covariance calculations can be simplified for better readability by reusing the computed means.

Suggested change

	function getStats(pts: Pt[]) {
	let sx = 0, sy = 0, sxx = 0, syy = 0, sxy = 0;
	const n = pts.length;
	for (let i = 0; i < n; i++) {
	const p = pts[i];
	sx += p.x; sy += p.y;
	sxx += p.x * p.x; syy += p.y * p.y; sxy += p.x * p.y;
	}
	const mx = sx / n, my = sy / n;
	return {
	m: { x: mx, y: my },
	sxx: (sxx - (sx * sx) / n) / n,
	syy: (syy - (sy * sy) / n) / n,
	sxy: (sxy - (sx * sy) / n) / n,
	};
	}
	function getStats(pts: Pt[]) {
	const n = pts.length;
	if (n === 0) return { m: { x: 0, y: 0 }, sxx: 0, syy: 0, sxy: 0 };
	let sx = 0, sy = 0, sxx = 0, syy = 0, sxy = 0;
	for (let i = 0; i < n; i++) {
	const p = pts[i];
	sx += p.x; sy += p.y;
	sxx += p.x * p.x; syy += p.y * p.y; sxy += p.x * p.y;
	}
	const mx = sx / n, my = sy / n;
	return {
	m: { x: mx, y: my },
	sxx: sxx / n - mx * mx,
	syy: syy / n - my * my,
	sxy: sxy / n - mx * my,
	};
	}

[gemini-code-assist]

The get1DStats function should include a guard for empty arrays to prevent division by zero. The standard deviation calculation can also be slightly simplified by using the already computed mean.

Suggested change

	function get1DStats(vals: number[]) {
	let s = 0, ss = 0;
	const n = vals.length;
	for (let i = 0; i < n; i++) {
	const v = vals[i];
	s += v; ss += v * v;
	}
	const mu = s / n;
	const sig = Math.sqrt(Math.max(0, (ss - (s * s) / n) / n));
	return { mu, sig };
	}
	function get1DStats(vals: number[]) {
	const n = vals.length;
	if (n === 0) return { mu: 0, sig: 0 };
	let s = 0, ss = 0;
	for (let i = 0; i < n; i++) {
	const v = vals[i];
	s += v; ss += v * v;
	}
	const mu = s / n;
	const sig = Math.sqrt(Math.max(0, ss / n - mu * mu));
	return { mu, sig };
	}