MichelNivard

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load the tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('./results')

# Set the device to GPU if available, otherwise use CPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

MichelNivard

MichelNivard

require(MASS)

# fixed a2 and e2 for the entire script:
a <- .87 # additive genetic variance
e <- .13 # environmental variance

# make ZM covariance, i.e. the cov is a, the var = 1
sigma_mz <- matrix(c(1,a,a,1),2,2) 

# sample size (is big becuase rare traits)

MichelNivard

## The problem with (personal) non-ordinality:

n <- 30000 # 10k fictional people

# a pair of exposures, no measurmennt issues:
xa <- rnorm(n)
xb <- rnorm(n)

# Personal threshold 1 - 4 for each person, 
# reasonable scale design I think by which I mean the bins fill up sort of "normal" like this matters a lot!!

MichelNivard

# t-test vs wilcox vs ordinal

library(tidyverse)
library(multidplyr) # parallelize
library(rms) # ordinal regression


sample_size = 500 # N

# genarate paired sets and calculate p-values with different techniques

MichelNivard

# Full training script for protein contact map diffusion model
# Using LucidRain's denoising-diffusion-pytorch (grayscale input)

from denoising_diffusion_pytorch import Unet, GaussianDiffusion, Trainer
import matplotlib.pyplot as plt
import numpy as np

model = Unet(

MichelNivard

import os
import numpy as np
import sidechainnet as scn
from denoising_diffusion_pytorch import Unet, GaussianDiffusion
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as T
from PIL import Image
from pathlib import Path
from tqdm import tqdm