rcdilorenzo

import numpy as np
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA

def covariance(M: np.ndarray) -> np.ndarray:
    """
    Compute sample covariance matrix from data M.
    
    NOTE: M is assumed to be of shape (nrows, ncols).
    

rcdilorenzo

import numpy as np
from typing import Callable, Optional, List
from pydantic import BaseModel
from tqdm.notebook import tqdm
from math import ceil, isnan, inf

class Activation(BaseModel):
    forward: Callable[[np.ndarray], np.ndarray] # input -> output
    backward: Callable[[np.ndarray, np.ndarray], np.ndarray] # (dA, X) -> gradients
        

rcdilorenzo

import * as t from 'io-ts';
import Ajv from 'ajv';
import addFormats from 'ajv-formats';
import { fromNullable } from 'io-ts-types/lib/fromNullable';
import convertToJSONSchema from './convertToJSONSchema';
import isoString from './isoString';

const SampleObject = t.strict(
  {
    aString: t.string,

rcdilorenzo

const puppeteer = require('puppeteer');
const read = require('read');
const htmlToText = require('html-to-text').fromString;
const R = require('ramda');
const Promise = require('bluebird');
const fs = require('fs');
const download = require('@jinphen/download2');
const { CookieJar } = require('tough-cookie');

const mapSeries = R.flip(Promise.mapSeries);

rcdilorenzo

// ========
// Usage
// ========
// > const { transposeObjects } = require('./utils');
// undefined
// > transposeObjects([
// ... { key1: 'object1key1', key2: 'object1key2' },
// ... { key1: 'object2key1', key2: 'object2key2' }
// ... ])
// { key1: [ 'object1key1', 'object2key1' ],

rcdilorenzo

#!/bin/bash
# resize-images.sh
# Preprocess Stanford Cars Dataset

# Stats for width
# ❯ ls cars_**/*.jpg |
#   xargs -L1 identify -format "%w\n" |
#   datamash min 1 max 1 mean 1 median 1
# 
# 78      7800    700.49255483472 640

rcdilorenzo

hist.density <- function (data, xlab = "<x>", font.main = 1,
                          main = "Histogram of data") {

  # Calculate histogram based on these values
  data.hist = hist(data, plot = FALSE)
  
  # Determine scaling factor
  multiplier = data.hist$counts / data.hist$density
  
  # Create density function of the area

rcdilorenzo

# Assumes an existing data frame called `raw`
# that is directly imported from the URL and
# has not converted the "?" values to NA's

colnames(raw) <- c("Type", "CapShape", "CapSurface", "CapColor", "Bruises",
                   "Odor", "GillAttachment", "GillSpacing", "GillSize",
                   "GillColor", "StalkShape", "StalkRoot",
                   "StalkSurfaceAboveRing", "StalkSurfaceBelowRing",
                   "StalkColorAboveRing", "StalkColorBelowRing", "VeilType",
                   "VeilColor", "RingNumber", "RingType", "SporePrintColor",

rcdilorenzo

from word_frequency import WordFrequency

luke = WordFrequency('Luke', './Luke.txt')
matthew = WordFrequency('Matt', './Matthew.txt')

print('================================')
print('= Frequently Occurring Phrases =')
print('= Between Matthew & Luke (Max  =')
print('= 4 words/phrase, Min freq 15) =')
print('================================')

rcdilorenzo

To read your settings, run:

defaults read ~/Library/Containers/com.getharvest.harvestxapp/Data/Library/Preferences/group.com.getharvest.Harvest.Documents.plist

To change the time format to HH:MM, run

defaults write ~/Library/Containers/com.getharvest.harvestxapp/Data/Library/Preferences/group.com.getharvest.Harvest.Documents.plist TimeFormat hours_minutes

Restart the Harvest App, and it should now display HH:MM (I tested this)