The API recognizes that synchronous data sources are both necessary and common. The application should not be forced to always accept the performance cost of asynchronous scheduling simply because that's the only option provided. At the same time, mixing sync and async processing can be dangerous. Synchronous paths should always be an option and should always be explicit.
const response = await fetch(url);
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One challenge is having enough training data. Another is that the training data needs to be free of contamination. For a model trained up till 1900, there needs to be no information from after 1900 that leaks into the data. Some metadata might have that kind of leakage. While it’s not possible to have zero leakage - there’s a shadow of the future on past data because what we store is a function of what we care about - it’s possible to have a very low level of leakage, sufficient for this to be interesting.
One by-product of weighing the candidates by their distance is that the resulting output image is prone to false contours or banding. Increasing reduces this effect at the cost of added granularity or high frequency noise due to the introduction of ever more distant colours to the set. I recommend taking a look at the original paper if you’re interested in learning a bit more about the algorithm[1].
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Opens in a new window。业内人士推荐雷电模拟器官方版本下载作为进阶阅读