m-root

import talib
import statsmodels.api as sm
import pandas as pd

def initialize(context):
    context.security = symbol('AAPL')
    #set_universe(universe.DollarVolumeUniverse(floor_percentile=98.0,ceiling_percentile=100.0))
    
def bar_data(OHLC_type, bars_nr):
    bar_data_func = (history((bars_nr + 1), '1d', OHLC_type).iloc[0]).astype('float')

kevvzub

import pandas as pd
from collections import Counter
import tensorflow as tf
from tffm import TFFMRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np

# Loading datasets'

gyglim

"""Simple example on how to log scalars and images to tensorboard without tensor ops.

License: BSD License 2.0
"""
__author__ = "Michael Gygli"

import tensorflow as tf
from StringIO import StringIO
import matplotlib.pyplot as plt
import numpy as np

lampts

# required tensorflow 0.12
# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word

from gensim.models import Word2Vec
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

# loading your gensim
model = Word2Vec.load("YOUR-MODEL")

philipperemy

from __future__ import print_function

import os

import numpy as np
from keras.layers import RepeatVector
from keras.layers.core import Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from keras.models import load_model

vadimkantorov

# Example:
# $ python argparse_dict_argument.py --env a=b --env aa=bb
# Namespace(env={'a': 'b', 'aa': 'bb'})

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--env', action = type('', (argparse.Action, ), dict(__call__ = lambda a, p, n, v, o: getattr(n, a.dest).update(dict([v.split('=')])))), default = {}) # anonymously subclassing argparse.Action
print(parser.parse_args())

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

fchollet

import theano
from keras.models import Sequential
from keras.layers.core import Dense, Activation

X_train, y_train = ... # load some training data
X_batch = ... # a batch of test data

# this is your initial model
model = Sequential()
model.add(Dense(20, 64))

mminer

"""An example of a cache decorator."""

import json
from functools import wraps
from redis import StrictRedis


redis = StrictRedis()

def cached(func):

bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np