| " Ensure correct syntax highlighting and auto-indentation for Fortran free-form | |
| " source code. | |
| let fortran_free_source=1 | |
| let fortran_do_enddo=1 | |
| filetype plugin indent on | |
| syntax on | |
| " Turn on line numbers and row/column numbers. | |
| set nu |
| #include <string.h> | |
| #include <dlfcn.h> | |
| #include <err.h> | |
| #include <errno.h> | |
| #include <sys/mman.h> | |
| #define LIB_PATH "libc.so.6" | |
| template <class RET, class... ARGV> | |
| class LibHook { |
| Lack is a minified Rack just for study. |
This article has been given a more permanent home on my blog. Also, since it was first written, the development of the Promises/A+ specification has made the original emphasis on Promises/A seem somewhat outdated.
Promises are a software abstraction that makes working with asynchronous operations much more pleasant. In the most basic definition, your code will move from continuation-passing style:
getTweetsFor("domenic", function (err, results) {
// the rest of your code goes here.| 更新: | 2024-10-08 |
|---|---|
| 作者: | @voluntas |
| バージョン: | 2024.1 |
| URL: | https://voluntas.github.io/ |
概要
| """ | |
| 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) |
| 更新: | 2024-05-20 |
|---|---|
| 作者: | @voluntas |
| バージョン: | 2024.1 |
| URL: | https://voluntas.github.io/ |
| # How to build and to run | |
| # mkdir build ; cd build/ ; cmake .. ; make ; ./test | |
| # (cleanup: rm -r build/) | |
| set(CMAKE_C_STANDARD 11) | |
| list(APPEND CMAKE_C_FLAGS "-Wall -Wextra -pedantic") | |
| add_library(mymalloc SHARED mymalloc.c) | |
| add_executable(test test.c) | |
| link_directories(.) | |
| target_link_libraries(test mymalloc) |
| """ 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 |
| """adapted from https://github.com/OlavHN/bnlstm to store separate population statistics per state""" | |
| import tensorflow as tf, numpy as np | |
| RNNCell = tf.nn.rnn_cell.RNNCell | |
| class BNLSTMCell(RNNCell): | |
| '''Batch normalized LSTM as described in arxiv.org/abs/1603.09025''' | |
| def __init__(self, num_units, is_training_tensor, max_bn_steps, initial_scale=0.1, activation=tf.tanh, decay=0.95): | |
| """ | |
| * max bn steps is the maximum number of steps for which to store separate population stats | |
| """ |
