bdhammel

import numpy as onp
from scipy.optimize import minimize
from scipy.stats import gaussian_kde
import matplotlib.pyplot as plt
import jax.numpy as np
from jax import random, lax
import numpyro
import numpyro.distributions as dist
from numpyro.infer import MCMC, NUTS

bdhammel

import numpy as np
from scipy.stats import norm, invgamma


# The barrel of apples
# The average apples is between 70-100 g
BARREL = np.random.normal(loc=85, scale=20, size=100)
# Grid
WEIGHT_GUESSES = np.linspace(1, 200, 100)
ERROR_GUESSES = np.linspace(.1, 50, 100)

bdhammel

# logging_example.py
"""
With the logging module imported, you can use something called a “logger” to log messages that you want to see. 
By default, there are 5 standard levels indicating the severity of events. 
Each has a corresponding method that can be used to log events at that level of severity. 
The defined levels, in order of increasing severity, are the following:
  - DEBUG
  - INFO
  - WARNING
  - ERROR

bdhammel

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
import argparse

bdhammel

class LinearRegression:
    
    def __init__(self, order):
        self.W = np.random.randn((order+1))
    
    def fit(self, X, Y, alpha=1e-5, epochs=1000):
        X = np.vstack((X, np.ones_like(X))).T
        Y = Y.T
        
        for _ in range(epochs):

bdhammel

import numpy as np
import matplotlib.pyplot as plt

BASE_LR = 1e-7
EPOCHS = 500
TRIALS = 3


class LRscanner: