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| """ | |
| The most atomic way to train and inference a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
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| #!/bin/bash | |
| # | |
| # custom-service Start up custom-service | |
| # | |
| # chkconfig: 2345 55 25 | |
| # description: the custom service (Python) | |
| # | |
| # processname: custom-service | |
| # Source function library |
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| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
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| import numpy as np | |
| import pylab as pl | |
| from numpy import fft | |
| def fourierExtrapolation(x, n_predict): | |
| n = x.size | |
| n_harm = 10 # number of harmonics in model | |
| t = np.arange(0, n) | |
| p = np.polyfit(t, x, 1) # find linear trend in x | |
| x_notrend = x - p[0] * t # detrended x |