juchiast

import numpy as np
from sklearn.linear_model import LinearRegression

def f(x):
    return 1 + 0.87*x[0] + 0.34*x[1] + 0.5*x[2]

X = np.random.random((2000, 3)) * 100
y = [f(x) for x in X]

lm = LinearRegression()

juchiast

extern crate rand;

use ::std::path::PathBuf;

fn random(low: u64, high: u64) -> u64 {
    low + rand::random::<u64>() % (high-low)
}

fn get_images_list(dir: &PathBuf) -> Vec<PathBuf> {
    let paths: Vec<_> = std::fs::read_dir(dir).unwrap()

juchiast

def rgb2lab (RGB):
   for value in RGB:
       value = value / 255
       if value > 0.04045:
           value = ((value + 0.055) / 1.055) ** 2.4
       else:
           value = value / 12.92
   XYZ = [0, 0, 0]
   XYZ[0] = (RGB[0] * 0.4124 + RGB[1] * 0.3576 + RGB[2] * 0.1805) / 95.047
   XYZ[1] = (RGB[0] * 0.2126 + RGB[1] * 0.7152 + RGB[2] * 0.0722) / 100

juchiast

/*
The MIT License (MIT)

Copyright (c) 2017 Do Duy

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

juchiast

/*
The MIT License (MIT)

Copyright (c) 2017 Do Duy

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is