kastnerkyle

# Author: Kyle Kaster
# License: BSD 3-clause
import numpy as np


def online_stats(X):
    """
    Converted from John D. Cook
    http://www.johndcook.com/blog/standard_deviation/
    """

kastnerkyle

# Author: Kyle Kastner
# License: BSD 3-clause
import numpy as np
from scipy.signal import lfilter
import matplotlib.pyplot as plt

sine = np.sin(np.linspace(-4 * np.pi, 4 * np.pi, 10000))
noise = 0.2 * np.random.randn(len(sine))
s = sine + noise

kastnerkyle

# Original code from tinrtgu on Kaggle under WTFPL license
# Relicensed to BSD 3-clause (it does say do what you want...)
# Authors: Kyle Kastner
# License: BSD 3-clause

# Reference links:
# Adaptive learning: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41159.pdf
# Criteo scalable response prediction: http://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf
# Vowpal Wabbit (hashing trick): https://github.com/JohnLangford/vowpal_wabbit/
# Hashing Trick: http://arxiv.org/pdf/0902.2206.pdf

kastnerkyle

# 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

kastnerkyle

# -*- coding: utf 8 -*-
# Author: Kyle Kastner
# License: BSD 3-clause
from __future__ import division
import os
import numpy as np
import tables
import numbers
import fnmatch

kastnerkyle

ffmpeg -i 111.mp3 -acodec pcm_s16le -ac 1 -ar 16000 out.wav
find ../mp3_symlinks/ -name *.mp3 -exec sh -c 'echo $(basename {} .mp3)' \;
find ../mp3_symlinks/ -name *.mp3 -exec sh -c 'ffmpeg -i {} -acodec pcm_s16le -ac 1 -ar 16000 $(basename {} .mp3).wav' \;

kastnerkyle

for f in *.ipynb ; do echo $f; aspell list < "$f" | sort | uniq -c ; done | less
2to3 -f imports folder/path | patch -p0

kastnerkyle

#!/usr/bin/env python
# Clone or update all a user's gists
# curl -ks https://raw.github.com/gist/5466075/gist-backup.py | USER=fedir python
# USER=fedir python gist-backup.py

import json
import urllib.request, urllib.parse, urllib.error
from subprocess import call
from urllib.request import urlopen
import os

kastnerkyle

import numpy as np
import theano
import theano.tensor as T
import pickle

class sgd_nesterov(object):
    def __init__(self, params):
        self.memory_ = [theano.shared(np.zeros_like(p.get_value()))
                        for p in params]

kastnerkyle

import numpy as np
import theano
import theano.tensor as T
import pickle

d = pickle.load(open("info.pkl", mode="rb"))

X = d["X"]
y = d["y"]
fit_function2 = d["fit_function"]