Shane Barratt sbarratt

## utoh.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Urls to Hyperlinks.

This script takes line-separated urls from stdin and writes formatted hyperlinks to stdout.

Usage:
# cat urls.txt | python ltoh.py > hyperlinks.

"""

## windows_multi.cpp
//more details: http://www.bogotobogo.com/cplusplus/multithreaded2A.php

#include <Windows.h>
#include <process.h>
#include <stdio.h>

unsigned int __stdcall test_thread(void *data) {
        printf("Thread Started");
        return 0;
}

## scrolltest.html
<html>
<head>
	<style>
		#content {
			font-size: 18;
			text-align: center;
			padding-top: 150px;
		}
		.blurry-text {
		   color: transparent;

## mlp.py
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import matplotlib.pyplot as plt

# Load data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

# Neural Network Initialization

## cnn.py
"""CNN from https://www.microsoft.com/en-us/research/wp-content/uploads/2003/08/icdar03.pdf"""

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

import tensorflow as tf

def weight_variable(shape):
    initial = tf.random_normal(shape, stddev=0.05)
    return tf.Variable(initial)

## 3per.py
import matplotlib.pyplot as plt
from numpy.fft import fft
from numpy.fft import fftshift
import numpy as np
import random

dictionary = ['A','C','G','T']

def generate_random_sequence(N):
	return [dictionary[random.randint(0,len(dictionary)-1)] for _ in range(N)]

## Presentation.ipynb

      
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                sbarratt
                / Presentation.ipynb
            
            
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              August 28, 2016 18:11
            
          
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## kmeans_missing.py
"""
Author: Shane Barratt
Email: sbarratt@stanford.edu
K-means script that works with NaN entries.
"""

import numpy as np
import IPython as ipy
import matplotlib.pyplot as plt

## flash.go
package main

import (
    "fmt"
    "log"
    "bytes"
    "math/big"
    "encoding/json"
    "io/ioutil"
    "net/http"

## erc20.py
from ctc import evm
from scipy import sparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

transfers = await evm.async_get_erc20_transfers(
    token='0x956f47f50a910163d8bf957cf5846d573e7f87ca',
    event_name='Transfer',
)
	#!/usr/bin/env python
	# -- coding: utf-8 --
	"""Urls to Hyperlinks.

	This script takes line-separated urls from stdin and writes formatted hyperlinks to stdout.

	Usage:
	# cat urls.txt \| python ltoh.py > hyperlinks.

	"""
	//more details: http://www.bogotobogo.com/cplusplus/multithreaded2A.php

	#include <Windows.h>
	#include <process.h>
	#include <stdio.h>

	unsigned int __stdcall test_thread(void *data) {
	printf("Thread Started");
	return 0;
	}
	<html>
	<head>
	<style>
	#content {
	font-size: 18;
	text-align: center;
	padding-top: 150px;
	}
	.blurry-text {
	color: transparent;
	from tensorflow.examples.tutorials.mnist import input_data
	import tensorflow as tf
	import matplotlib.pyplot as plt

	# Load data

	mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

	# Neural Network Initialization
	"""CNN from https://www.microsoft.com/en-us/research/wp-content/uploads/2003/08/icdar03.pdf"""

	from tensorflow.examples.tutorials.mnist import input_data
	mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

	import tensorflow as tf

	def weight_variable(shape):
	initial = tf.random_normal(shape, stddev=0.05)
	return tf.Variable(initial)
	import matplotlib.pyplot as plt
	from numpy.fft import fft
	from numpy.fft import fftshift
	import numpy as np
	import random

	dictionary = ['A','C','G','T']

	def generate_random_sequence(N):
	return [dictionary[random.randint(0,len(dictionary)-1)] for _ in range(N)]
	"""
	Author: Shane Barratt
	Email: sbarratt@stanford.edu
	K-means script that works with NaN entries.
	"""

	import numpy as np
	import IPython as ipy
	import matplotlib.pyplot as plt
	package main

	import (
	"fmt"
	"log"
	"bytes"
	"math/big"
	"encoding/json"
	"io/ioutil"
	"net/http"
	from ctc import evm
	from scipy import sparse
	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt

	transfers = await evm.async_get_erc20_transfers(
	token='0x956f47f50a910163d8bf957cf5846d573e7f87ca',
	event_name='Transfer',
	)