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"""
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)

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import numpy as np
np.set_printoptions(precision=3)

# inputs
N = 2
E = 100
S = 0.5
nu = 0.2
A = np.array([[1,2,4],[4,3,1]])
xy = np.array([[0,0],[1,0],[0,1],[1,1]]) # coordinates [xi,yi]

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from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import svrg_solver

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {

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function ww = approx_int_func_solve(xy_pairs, xmin, xmax, intval, w0, f, lbda)
% example: 
% xy_pairs = [0, 1.32; 33.9, .55];
% xmin = 0;
% xmax = 33.9;
% intval = 38.985;
% w0 = [0.1,-2,2]
% ww = approx_int_func_solve(xy_pairs, xmin, xmax, intval, w0);

if nargin < 6

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