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Lisp interpreter in 90 lines of C++ | |
I've enjoyed reading Peter Norvig's recent articles on Lisp. He implements a Scheme interpreter in 90 lines of Python in the first, and develops it further in the second. | |
Just for fun I wondered if I could write one in C++. My goals would be | |
1. A Lisp interpreter that would complete Peter's Lis.py test cases correctly... | |
2. ...in no more than 90 lines of C++. | |
Although I've been thinking about this for a few weeks, as I write this I have not written a line of the code. I'm pretty sure I will achieve 1, and 2 will be... a piece of cake! |
""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
import theano | |
import theano.tensor as T | |
from theano.tensor.shared_randomstreams import RandomStreams | |
from theano.sandbox.rng_mrg import MRG_RandomStreams | |
from lasagne.updates import adam | |
from lasagne.utils import collect_shared_vars | |
from sklearn.datasets import fetch_mldata | |
from sklearn.cross_validation import train_test_split | |
from sklearn import preprocessing |
""" 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 |
import logging | |
import numpy as np | |
import tensorflow as tf | |
from tensorflow.contrib import layers | |
GO_TOKEN = 0 | |
END_TOKEN = 1 | |
UNK_TOKEN = 2 |
# coding=utf-8 | |
# Copyright 2017 The Tensor2Tensor Authors. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software |