octocat

# Compiled source #
###################
*.com
*.class
*.dll
*.exe
*.o
*.so

# Packages #

karpathy

"""
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)

karpathy

""" 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

aparrish

slightfoot

// Copyright 2015 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.

import 'dart:async';

import 'package:flutter/foundation.dart';
import 'package:flutter/material.dart';
import 'package:flutter/widgets.dart';

tdietert

global account issuer = 'CssRnWaxBhRRwhVL7ESgXkP7cgZ9vjFzV5nmr4jj3ZAh';
global account borrower = 'vVVq8PMEa9qGLL7LEmHUZKM78ze5JUgDPnjSE6FES63';
global assetFrac2 asset_ = 'HjdMu5LtF7BW6pGzvZGsV7ABUA4dkZFB2TmXQiymNpue';
global fixed2 interest= 0.05f;
global fixed2 principal;
global fixed2 payout;

global datetime maturityDate = "2018-03-08T10:12:00+00:00";

transition initial -> amountConfirmed;

Thomascountz

"""
MIT License

Copyright (c) 2018 Thomas Countz

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

arsalanaf

from btgym import BTgymEnv
import IPython.display as Display
import PIL.Image as Image
from gym import spaces


import gym
import numpy as np
import random

dvgodoy

torch.manual_seed(42)

x_tensor = torch.from_numpy(x).float()
y_tensor = torch.from_numpy(y).float()

# Builds dataset with ALL data
dataset = TensorDataset(x_tensor, y_tensor)
# Splits randomly into train and validation datasets
train_dataset, val_dataset = random_split(dataset, [80, 20])

apalepu23