Robert Tjarko Lange RobertTLange

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RobertTLange / twitter.py

Last active November 15, 2024 17:09

Twitter API Example

	import tweepy
	import os
	from typing import List, Optional


	class TwitterClient:
	def __init__(
	self,
	consumer_key: str,
	consumer_secret: str,

RobertTLange / jax_ou_process.py

Created March 5, 2021 19:32

Ornstein-Uhlenbeck Process in JAX

	import jax
	import jax.numpy as jnp

	def ou_process(key, steps, dt, mu, tau, sigma):
	""" Generate an Ornstein-Uhlenbeck process sample. """
	ou_init = jnp.zeros((steps + 1, ))
	noise = jax.random.normal(key, (steps,))
	def ou_step(t, val):
	dx = (-(val[t-1]-mu)/tau * dt
	+ sigmajnp.sqrt(2/tau)

RobertTLange / bce.py

Created September 1, 2019 14:33

	def binary_cross_entropy(y_true, y_pred):
	y_true = np.append(1 - y_true, y_true, axis=1)
	y_pred = np.append(1 - y_pred, y_pred, axis=1)
	bce = -(y_true * np.log(y_pred)).sum(axis=1)
	return bce

RobertTLange / ad_loop.py

Last active September 1, 2019 14:47

	def train_logistic_regression(n, d, n_epoch, batch_size, b_init, l_rate):
	# Generate the data for a coefficient vector & init progress tracker!
	data_loader = DataLoader(n, d, batch_size, binary=True)
	b_hist, func_val_hist, param_error, acc_hist = [], [], [], []

	# Get the coefficients as solution to optimized sklearn function
	logreg = LogisticRegression(penalty='none', solver='lbfgs', multi_class='multinomial')
	logreg.fit(data_loader.X, data_loader.y)
	norm_coeff = np.linalg.norm(logreg.coef_.ravel())

RobertTLange / dual_logistic_regression.py

Created August 29, 2019 14:33

Logistic Regression with SGD using Forward cumulative mode + Dual Numbers

	import numpy as np
	from sklearn.linear_model import LogisticRegression

	class DataLoader(object):
	# Small class object for dual number - Two real numbers (real & dual part)
	def __init__(self, n, d, batch_size, binary=False):
	self.total_dim = d + 1
	self.X, self.y = self.generate_regression_data(n, d, binary)

	# Set batch_id for different indices