agramfort

"""
This module implements the Lowess function for nonparametric regression.

Functions:
lowess Fit a smooth nonparametric regression curve to a scatterplot.

For more information, see

William S. Cleveland: "Robust locally weighted regression and smoothing
scatterplots", Journal of the American Statistical Association, December 1979,

eskriett

#!/usr/bin/python
# GoogleMapDownloader.py 
# Created by Hayden Eskriett [http://eskriett.com]
#
# A script which when given a longitude, latitude and zoom level downloads a
# high resolution google map
# Find the associated blog post at: http://blog.eskriett.com/2013/07/19/downloading-google-maps/

import urllib
import Image

tyrcho

• Install the Scala plugin for Eclipse from Scala IDE. I use this update-site for scala 2.11 : http://download.scala-ide.org/sdk/lithium/e44/scala211/stable/site

• Install this eclipse plugin for maven / m2e : http://alchim31.free.fr/m2e-scala/update-site

• You can then import easily a Maven project from the provided pom.xml file

• Right-click on the project, Configure > Add Scala Nature

soumith

----------------------------------------------------------------------
-- CIFAR 8x8
opt.scale = 8 
opt.geometry = {3, opt.scale, opt.scale}
local input_sz = opt.geometry[1] * opt.geometry[2] * opt.geometry[3]
local numhid = 600 
model_D = nn.Sequential()
model_D:add(nn.Reshape(input_sz))
model_D:add(nn.Linear(input_sz, numhid))
model_D:add(nn.ReLU())

karpathy

"""
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)

dump_weights_hdf5.py

# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

MatsuuraKentaro

data {
  int N;
  int M;
  real<lower=0> Y[N];
}

parameters {
  real<lower=0> mu;
  real<lower=0> phi;
  real<lower=1, upper=2> theta;

siemanko

"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:

yoyama

/**
  * Generate Case class from DataFrame.schema
  *
  *  val df:DataFrame = ...
  *
  *  val s2cc = new Schema2CaseClass
  *  import s2cc.implicit._
  *
  *  println(s2cc.schemaToCaseClass(df.schema, "MyClass"))
  *

akelleh