milespossing

{
  "version": 1,
  "notes": "",
  "documentation": "\"This file is a QMK Configurator export. You can import this at <https://config.qmk.fm>. It can also be used directly with QMK's source code.\n\nTo setup your QMK environment check out the tutorial: <https://docs.qmk.fm/#/newbs>\n\nYou can convert this file to a keymap.c using this command: `qmk json2c {keymap}`\n\nYou can compile this keymap using this command: `qmk compile {keymap}`\"\n",
  "keyboard": "planck/rev6",
  "keymap": "planck_rev6_layout_ortho_4x12_mine",
  "layout": "LAYOUT_ortho_4x12",
  "layers": [
    [
      "KC_TAB",

milespossing

import numpy as np
from sklearn.feature_extraction.text import CountVectorizer

all_data = pd.read_table('alldata.tsv')
dict = {pair[1]['token']:pair[1]['coef'] for pair in all_vocab[['token','coef']].iterrows()}

vectorizer = CountVectorizer(vocabulary=all_vocab['token'].values, max_df=0.3, ngram_range=(1, 2), min_df=20)
cur_vocab = vectorizer.get_feature_names()

def get_tokens(review):

milespossing

use amethyst::{
    ecs::{System, WriteStorage, Read, Join},
    core::timing::Time,
};
use crate::rays::Ray;

pub struct PositionSystem;

impl<'a> System<'a> for PositionSystem {
    type SystemData = WriteStorage<'a, Ray>;

milespossing

use amethyst::{
    core::math::{Vector3},
    ecs::{Component, VecStorage},
};

#[derive(Debug)]
pub struct Ray {
    pub pos: Vector3<f32>,
    pub vel: Vector3<f32>,
}

milespossing

Test