dapurv5

$ python xor.py
Training:
  Epoch   0   MSE: 1.765
  Epoch 100   MSE: 0.015
  Epoch 200   MSE: 0.005
  * Target MSE reached *
Evaluating:
  1 XOR 0 = 1 ( 0.904)   Error: 0.096
  0 XOR 1 = 1 ( 0.908)   Error: 0.092
  1 XOR 1 = 0 (-0.008)   Error: 0.008

dapurv5

from __future__ import print_function, division
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

num_epochs = 100
total_series_length = 50000
truncated_backprop_length = 15
state_size = 4
num_classes = 2

dapurv5

"""
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)

dapurv5

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

dapurv5

"""
client.py - AsyncIO Server using StreamReader and StreamWriter

This will create 200 client connections to a server running server.py

It will handshake and run similar to this:

Server: HELLO
Client: WORLD