jperla

# Incomplete row major matrix implementation.
type RowMajorMatrix{T} <: AbstractMatrix{T}
    col_major::Matrix{T}
end

row_major{T}(c::Matrix{T}) = RowMajorMatrix{T}(c)
Base.size{T}(m::RowMajorMatrix{T}, n::Int) = if n == 1 size(m.col_major, 2) elseif n == 2 size(m.col_major, 1) else size(m.col_major, n) end

Base.getindex{T}(m::RowMajorMatrix{T}, i, j) = m.col_major[j, i]
function setindex!{T}(m::RowMajorMatrix{T}, value, i, j)

hemanth

require 'oauth'
require 'launchy'
require 'nokogiri'


class User
  def authenticate(consumer_key, consumer_secret, site)
    consumer = OAuth::Consumer.new(consumer_key, consumer_secret, site)
    request_token = consumer.get_request_token
    puts "Authorize Follower and then paste in the PIN:"

skryv

      echo "-----> Installing via pip, but setting environment vars first."
      export BLAS=$(pwd)/vendor/lib/atlas-base/atlas/libblas.a
      export LAPACK=$(pwd)/vendor/lib/atlas-base/atlas/liblapack.a
      export ATLAS=$(pwd)/vendor/lib/atlas-base/libatlas.a
      export LIBRARY_PATH=$(pwd)/vendor/lib:$(pwd)/lib/atlas-base:$(pwd)/lib/atlas-base/atlas
      export LD_LIBRARY_PATH=$(pwd)/vendor/lib:$(pwd)/vendor/lib/atlas-base:$(pwd)/vendor/lib/atlas-base/atlas

      pip install --use-mirrors scikit-learn

skryv

heroku config:add BUILDPACK_URL=https://github.com/ToonTimbermont/heroku-buildpack-python

shyamupa

def get_H_n(X):
    return X[:, -1, :]  # get last element from time dim


def get_Y(X):
    return X[:, :110, :]  # get first xmaxlen elem from time dim

def get_R(X):
    Y, alpha = X.values()  # Y should be (L,k) and alpha should be (L,) and ans should be (k,)

sbos

module HMM

using Distributions

import Distributions.rand
import Distributions.fit

immutable HiddenMarkovModel{TP, K}
    theta::Vector{TP}
    A::Matrix{Float64}

jiumem

class LossHistory(Callback):
    def __init__(self, X_train, y_train, layer_index):
        super(Callback, self).__init__()
        self.layer_index = layer_index
        if X_train.shape[0] >= 1000:
            mask = np.random.choice(X_train.shape[0], 1000)
            self.X_train_subset = X_train[mask]
            self.y_train_subset = y_train[mask]
        else:

elmarhaussmann

# Based on the example from the TensorFlow repository: https://github.com/tensorflow/tensorflow/
# https://github.com/tensorflow/tensorflow/blob/671baf080238025da9698ea980cd9504005f727c/tensorflow/examples/learn/text_classification_character_rnn.py


from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

elijahmanor

describe("getTweets - Server", function () {
    var server, fakeData = [ /* ... */ ];

    before(function () {
        // Doesn’t work :( It’s JSONP!
        server = sinon.fakeServer.create();
        server.respondWith(
            "GET", 
            "https://api.twitter.com/.../elijahmanor.json?count=5",
            [200, { "Content-Type": "application/json" }, JSON.stringify(fakeData)]

mokemokechicken

#!/usr/bin/env python
# coding: utf-8
"""Sampling Sequence Data from model"""

import numpy as np
import tensorflow as tf
import json
import cPickle as pickle
import itertools as it
from rnnlib import PTBModel