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View pooling_python.py
from multiprocessing import Pool
# parallelize function
def product(a,b):
print a*b
# auxiliary funciton to make it work
def product_helper(args):
return product(*args)
View klp_kmeans.py
import numpy as np
import theano
import theano.tensor as T
from theano import function,sandbox
from theano import ProfileMode
import warnings
warnings.filterwarnings("ignore")
# Dummy Data
theano.config.floatX = 'float32' # Theano needs this type of data for GPU use
@erogol
erogol / ZCA_whitening.py
Last active August 29, 2015 13:57 — forked from dmaniry/gist:5170087
ZCA whitening of given data matrix
View ZCA_whitening.py
import numpy as np
from scipy import linalg
from sklearn.utils import array2d, as_float_array
from sklearn.base import TransformerMixin, BaseEstimator
class ZCA(BaseEstimator, TransformerMixin):
def __init__(self, regularization=10**-5, copy=False):
self.regularization = regularization
View tmux_cheatsheet.markdown

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

@erogol
erogol / call.py
Created April 22, 2014 08:07
sklearn call
View call.py
if __name__ == '__main__':
from sklearn.cross_validation import StratifiedShuffleSplit
# svc = LinearSVC(class_weight='auto', penalty='l1', dual=False, verbose = 15, C=0.1)
svc = Perceptron(penalty='l1', alpha=0.05, fit_intercept=True, n_iter = 100, shuffle=True, n_jobs=-1, class_weight='auto', verbose=15)
sf = StratifiedShuffleSplit(y,n_iter = 2, train_size =0.9, test_size=0.1);
for train,test in sf:
X_train, X_test = X[train], X[test]
y_train, y_test = y[train], y[test]
print 'Train set size: ', X_train.shape
print 'Test set size: ', X_test.shape
@erogol
erogol / bing_scrap.py
Created May 29, 2014 10:07
bing scrabber but add more to query address
View bing_scrap.py
#!/bin/bash
from bs4 import BeautifulSoup
import requests
import urllib2
import os
import re, urlparse
import time
import pdb
from interruptingcow import timeout
View pandas_export_to_mysql.py
import pandas as pd
from sqlalchemy import *
engine = create_engine('mysql://username:pass@server_address', pool_recycle=60)
DF = pd.io.parsers.read_csv('csv/file/path.csv')
DF = DF.dropna()
DF.to_sql('table_name', con=engine, if_exists='replace', flavor='mysql') # replace truncates the existing table and creates a new one
@erogol
erogol / svd_whiten.py
Created November 19, 2014 16:28
svd based whitining
View svd_whiten.py
def svd_whiten(X):
U, s, Vt = np.linalg.svd(X)
# U and Vt are the singular matrices, and s contains the singular values.
# Since the rows of both U and Vt are orthonormal vectors, then U * Vt
# will be white
X_white = np.dot(U, Vt)
return X_white
@erogol
erogol / gist:78fe47aaa31cd9c72f10
Created December 23, 2014 09:00
my_model.prototxt
View gist:78fe47aaa31cd9c72f10
name: "sentio_full_train"
# N.B. input image must be in CIFAR-10 format
# as described at http://www.cs.toronto.edu/~kriz/cifar.html
layers{
name: "data"
type: HDF5_DATA
top: "data"
top: "label"
hdf5_data_param {
View adagrad_rmsprop_momentum_compare
{
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"name": "",
"signature": "sha256:7278f115593dc43b248938b9210d9794dad8be21e1221b4b92f49716c2b633b8"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [