spro

import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
from torch import optim
import numpy as np
import math, random

# Generating a noisy multi-sin wave 

martinraison

import argparse
from collections import Counter
import csv
import os
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import tarfile

Mevrael

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;
use Illuminate\Support\Facades\Auth;
use Illuminate\Support\Facades\Cookie;
use Illuminate\Support\Facades\Mail;
use Illuminate\Support\Facades\Session;
use Ratchet\WebSocket\Version\RFC6455\Connection;

tejaslodaya

import multiprocessing
import pandas as pd
import numpy as np

def _apply_df(args):
    df, func, num, kwargs = args
    return num, df.apply(func, **kwargs)

def apply_by_multiprocessing(df,func,**kwargs):
  workers=kwargs.pop('workers')

diegslva

# Time Series Testing 
import keras.callbacks
from keras.models import Sequential  
from keras.layers.core import Dense, Activation, Dense, Dropout
from keras.layers.recurrent import LSTM

# Call back to capture losses 
class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = []

philipperemy

from __future__ import print_function

import numpy as np
from keras.callbacks import Callback
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy.random import choice

from utils import prepare_sequences

lukovkin

import numpy as np
import pandas as pd


def compute_market_prices(prices):
    """Compute market prices according to the trading competition recipe.

    Parameters
    ----------
    prices : DataFrame

teoliphant

import numpy as np

def array_for_sliding_window(x, wshape):
    """Build a sliding-window representation of x.

    The last dimension(s) of the output array contain the data of
    the specific window.  The number of dimensions in the output is 
    twice that of the input.

    Parameters

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

TomAugspurger