[MXNLP-1-10] Word2Vec - [8]: Implementation of a Skip-Gram model with Negative Sampling.
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Nothing Fancy... just experimenting and exploring with the available ones before exploring the new trending ones.. The core trick here: ctx.measureText() pre-computes every word into a data object with a baseX, current x, and targetX. Each frame, a 120-segment dragon emits collision nodes with anatomically-computed radii. For every line of text, it projects each node onto the line's Y axis, compute the X exclusion span via √(r² - dy²), merge overlapping intervals, then run a relaxation solver (f
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Nothing Fancy... just experimenting and exploring with the available ones before exploring the new trending ones.. The core trick here: ctx.measureText() pre-computes every word into a data object with a baseX, current x, and targetX. Each frame, a 120-segment dragon emits collision nodes with anatomically-computed radii. For every line of text, it projects each node onto the line's Y axis, compute the X exclusion span via √(r² - dy²), merge overlapping intervals, then run a relaxation solver (f
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