messefor

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "private_outputs": true,
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {

messefor

messefor

#! /usr/bin/env python
"""
Convert IMDB META data(matlab .mat) to csv.

IMDB-WIKI – 500k+ face images with age and gender labels
- https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/
- https://stackoverflow.com/questions/13965740/converting-matlabs-datenum-format-to-python
"""

from datetime import datetime, timedelta

messefor

messefor

#! /bin/bash
# Compare all files in the first directory with those of the second directory

dir1=src1/  # 1st Directory
dir2=src2/  # 2nd Directory

fpaths=${dir1}*  # Fetch all file paths in the 1st directory

# Loop for all files
i=0

messefor

data {
  int N;
  vector[N] x;
  vector[N] y;
}

parameters {
  real<lower=0> a;
  real<lower=0> phi;  // variance of gamma
  real<lower=0> b0;

messefor

# Simulate exponential data / gamma distribution with stan

library(rstan)
library(tidyverse)

options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)


# -----------------------------------------------------------

messefor

import time
import numpy as np
import pandas as pd
import seaborn as sns


x = [1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 6]

fig, ax = plt.subplots()

messefor

"""AR(1) Process Simulation """
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.graphics.tsaplots import plot_acf


def gen_ar1(phi, N=1000):
    """Simulate AR(1) process"""

    Z = np.random.normal(0, 1, N)  # Gaussian W.N.

messefor

"""Convertace article with TEX syntax with Hatena Blog Syntax """
import os
import re
from typing import List, Callable
from copy import deepcopy


def replace_m(x: str, rep_map: List[dict]) -> str:
    for pattern, repl in rep_map:
        x = x.replace(pattern, repl)