example.md

title	project	image	author	dateCreated	dateModified	categories	tags	featured	draft
Lorem ipsum dolor sit amet	Lorem One	src alt citation nick-morrison-FHnnjk1Yj7Y-unsplash.jpg Post Image Photo by <a href='https://unsplash.com/@nickmorrison?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText'>Nick Morrison</a> on <a href='https://unsplash.com/?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText'>Unsplash</a>	name url Jo Doe /pages/about	1/8/2021		Photography	Art Image Random	true	true

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque convallis purus vitae ipsum imperdiet condimentum. Nunc vel vehicula augue. Vestibulum luctus nulla nisl, at congue augue maximus et. Mauris eget massa dui. Cras sagittis accumsan lacus.

Table of Content

Install

An example JavaScript code.

const srcPath = "./articles/";
const dstPath = "./content/posts/";

fs.watch(srcPath, { persistent: true }, function (event, filename) {
  if ((event = "change")) {
    const srcFile = filename;
    const dstFile = srcFile.split(".")[0] + ".json";

    processor.process(
      toVFile.readSync(srcPath + srcFile, "utf8"),
      function (error, file) {
        if (error) throw error;
        console.error(reporter(error || file));

        let jsonOutput = {
          schema: file.data.frontmatter,
          body: file.contents,
        };

        let article = JSON.stringify(jsonOutput).replace(/\n/g, " ");
        // console.log(article);
        fs.writeFileSync(dstPath + dstFile, article);
      }
    );
  }
});

A Python code sample.

if __name__ == "__main__":
  import doctest

  doctest.testmod()

  network = Perceptron(
    sample=samples, target=exit, learning_rate=0.01, epoch_number=1000, bias=-1
  )
  network.training()
  print("Finished training perceptron")
  print("Enter values to predict or q to exit")
  while True:
    sample: list = []
    for i in range(len(samples[0])):
      user_input = input("value: ").strip()
      if user_input == "q":
        break
      observation = float(user_input)
      sample.insert(i, observation)
    network.sort(sample)

Math

$$ L = \frac{1}{2} \rho v^2 S C_L $$

License

MIT