Code for Keras plays catch blog post
python qlearn.pypython test.py| #!/usr/bin/env bash | |
| # | |
| # Fix virtualenv symlinks after upgrading python with Homebrew and then running | |
| # `cleanup`. | |
| # | |
| # After upgrading Python using Homebrew and then running `brew cleanup` one can | |
| # get this message while trying to run python: | |
| # dyld: Library not loaded: @executable_path/../.Python | |
| # Referenced from: /Users/pablo/.venv/my-app/bin/python | |
| # Reason: image not found |
| """ | |
| 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) |
Code for Keras plays catch blog post
python qlearn.pypython test.py| # to generate your dhparam.pem file, run in the terminal | |
| openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048 |
| package project2; | |
| import java.io.*; | |
| public class problem2 { | |
| public static void main(String[] args) throws Exception { | |
| int rnum[] = new int[11]; | |
| int inum[] = new int[11]; | |
| String ist[] = new String[11]; | |
| double rd[] = new double[11]; | |
| String slts; |
| """ Poisson-loss Factorization Machines with Numba""" | |
| # Author: Vlad Niculae <[email protected]> | |
| # License: Simplified BSD | |
| from __future__ import print_function | |
| import numpy as np | |
| from numba import jit | |
| from scipy import sparse as sp | |
| from sklearn.utils import check_random_state, check_array, check_X_y |
| import numpy as np | |
| from numpy.random import choice | |
| def stirling(N, m): | |
| if N < 0 or m < 0: | |
| raise Exception("Bad input to stirling.") | |
| if m == 0 and N > 0: | |
| return 0 | |
| elif (N, m) == (0, 0): | |
| return 1 |
| """ | |
| This is a batched LSTM forward and backward pass | |
| """ | |
| import numpy as np | |
| import code | |
| class LSTM: | |
| @staticmethod | |
| def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
| # Authors: Kyle Kastner | |
| # License: BSD 3-clause | |
| import theano.tensor as T | |
| import numpy as np | |
| import theano | |
| class rmsprop(object): | |
| """ | |
| RMSProp with nesterov momentum and gradient rescaling |