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| 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() |
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| # Simulate exponential data / gamma distribution with stan | |
| library(rstan) | |
| library(tidyverse) | |
| options(mc.cores = parallel::detectCores()) | |
| rstan_options(auto_write = TRUE) | |
| # ----------------------------------------------------------- |
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| 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; |
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| #! /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 |
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "private_outputs": true, | |
| "provenance": [], | |
| "gpuType": "T4" | |
| }, | |
| "kernelspec": { |
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