endolith

Written by ChatGPT and me.

I also had to enable temperhum as a non-root user and set up a cron job to call this script every once in a while.

FINALLY.

Instructions:

To use it:

My first keras experiments:

simplest.py — A single neuron with no activation function and no bias (so just input→weight→output, can learn y=mx functions only). Show the function for a few random weights. (Doesn't learn anything.)
linear with bias.py — A single neuron with no activation function but with bias (so it can learn y=mx+b functions, such as relationship between Celsius and Fahrenheit).
single_neuron_buffer.py — A single neuron with sigmoid activation and no bias, trained to learn a digital logic buffer.
single_neuron_inverter.py — A single neuron with sigmoid activation and no bias, trained to learn an inverting analog amplifier.

	"""
	Break up a long recording to fit within the Whisper API's limits, with some
	overlap, so no words are missed, and then feed to OpenAI Whisper API to
	transcribe it to .txt file. Written by endolith and ChatGPT-4.
	"""

	import openai
	import math
	import os
	import subprocess

	import numpy as np
	import matplotlib.pyplot as plt
	from scipy.ndimage import gaussian_filter

	rng = np.random.default_rng()

	# shape = (1024, )
	# shape = (512, 512)
	shape = (128, 128, 128)

	"""
	Written by GPT3 from this English prompt (with one tweak to the prompt, to get
	the timestamp parameter to not error out, and PEP8 fixes):

	----

	1. Open a sqlite database with a name like `2022-07-17 17.48.19.db` or
	`2021-04-11 08.44.16.db`
	2. Inside is a table called `locations`, with headers [LOCTYPE INTEGER,
	TIMESTAMP BIGINT, LATITUDE REAL, LONGITUDE REAL,ALTITUDE REAL, ACCURACY

	"""
	Confirm the formula for estimating the variance of an estimated probability.

	Created on Fri May 27 11:06:43 2022
	"""
	import numpy as np


	def func(x):
	"""

	"""
	Train a neural network to learn an FIR filter.

	Created on Fri Aug 3 15:00:40 2018
	"""
	from tensorflow.keras.models import Sequential
	from tensorflow.keras.layers import Dense
	from tensorflow.keras.callbacks import Callback
	import numpy as np
	from scipy import signal

	""" Zoom FFT function"""

	import numpy as np
	from time import time
	from scipy.fftpack import fft, ifft
	from numpy import swapaxes

	def chirpz(x, A=None, W=None, M=None):
	"""chirpz(x, A, W, M) - Chirp z-transform of variable x

	python 3.8.*
	scikit-learn ==0.24.1