Sungjoo Ha shurain

## fix-venv.sh
#!/usr/bin/env bash
#
# Fix virtualenv symlinks after upgrading python with Homebrew and then running
# `cleanup`.
#
# After upgrading Python using Homebrew and then running `brew cleanup` one can
# get this message while trying to run python:
# dyld: Library not loaded: @executable_path/../.Python
#   Referenced from: /Users/pablo/.venv/my-app/bin/python
#   Reason: image not found

## min-char-rnn.py
"""
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)

## README.md

      
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                shurain
                / README.md
            
            
              Created
              March 19, 2016 14:40
                — forked from EderSantana/CATCH_Keras_RL.md
            
              
                Keras plays catch - a single file Reinforcement Learning example
              
          
    Code for Keras plays catch blog post
Train

python qlearn.py
Generate figures

python test.py

  
## arxiv2kindle.ipynb

      
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                shurain
                / arxiv2kindle.ipynb
            
            
              Created
              January 16, 2016 15:19
                — forked from bshillingford/arxiv2kindle.ipynb
            
              
                arxiv2kindle: recompiles an arxiv paper for kindle-sized screens, and sends it to your wifi-enabled kindle
              
          
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## nginx.conf
# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048

## problem2-1.java
package project2;

import java.io.*;
public class problem2 {
	public static void main(String[] args) throws Exception {
		int rnum[] = new int[11];
		int inum[] = new int[11];
		String ist[] = new String[11];
		double rd[] = new double[11];
		String slts;

## poisson_factorization_machine.py
""" Poisson-loss Factorization Machines with Numba"""
# Author: Vlad Niculae <vlad@vene.ro>
# License: Simplified BSD

from __future__ import print_function
import numpy as np
from numba import jit
from scipy import sparse as sp

from sklearn.utils import check_random_state, check_array, check_X_y

## antoniak.py
import numpy as np
from numpy.random import choice

def stirling(N, m):
    if N < 0 or m < 0:
        raise Exception("Bad input to stirling.")
    if m == 0 and N > 0:
        return 0
    elif (N, m) == (0, 0):
        return 1

## gist:d26d44d7ef0ea1908b10
"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:

  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

## optimizers.py
# Authors: Kyle Kastner
# License: BSD 3-clause
import theano.tensor as T
import numpy as np
import theano


class rmsprop(object):
    """
    RMSProp with nesterov momentum and gradient rescaling
	#!/usr/bin/env bash
	#
	# Fix virtualenv symlinks after upgrading python with Homebrew and then running
	# `cleanup`.
	#
	# After upgrading Python using Homebrew and then running `brew cleanup` one can
	# get this message while trying to run python:
	# dyld: Library not loaded: @executable_path/../.Python
	# Referenced from: /Users/pablo/.venv/my-app/bin/python
	# Reason: image not found
	"""
	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)
	# to generate your dhparam.pem file, run in the terminal
	openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048
	package project2;

	import java.io.*;
	public class problem2 {
	public static void main(String[] args) throws Exception {
	int rnum[] = new int[11];
	int inum[] = new int[11];
	String ist[] = new String[11];
	double rd[] = new double[11];
	String slts;
	""" Poisson-loss Factorization Machines with Numba"""
	# Author: Vlad Niculae <vlad@vene.ro>
	# License: Simplified BSD

	from __future__ import print_function
	import numpy as np
	from numba import jit
	from scipy import sparse as sp

	from sklearn.utils import check_random_state, check_array, check_X_y
	import numpy as np
	from numpy.random import choice

	def stirling(N, m):
	if N < 0 or m < 0:
	raise Exception("Bad input to stirling.")
	if m == 0 and N > 0:
	return 0
	elif (N, m) == (0, 0):
	return 1
	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):
	# Authors: Kyle Kastner
	# License: BSD 3-clause
	import theano.tensor as T
	import numpy as np
	import theano


	class rmsprop(object):
	"""
	RMSProp with nesterov momentum and gradient rescaling