vitasiku

## Simple Python module to upload files to Google Drive
# Needs a file 'client_secrets.json' in the directory 
# The file can be obtained from https://console.developers.google.com/ 
# under APIs&Auth/Credentials/Create Client ID for native application

# To test usage:
# import google_drive_util
# google_drive_util.login()
# google_drive_util.test()

vitasiku

Building TensorFlow from source

The official instructions on building TensorFlow are here: https://www.tensorflow.org/install/install_sources

Prerequisites

We are assuming a build with CUDA support, as well as including SIMD optimizations (SSE3, SSE4, AVX, AVX2, FMA).

Good to know: The compute capabilities for

• Maxwell TITAN X: 5.2

vitasiku

#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda

vitasiku

So you've cloned somebody's repo from github, but now you want to fork it and contribute back. Never fear!

Technically, when you fork "origin" should be your fork and "upstream" should be the project you forked; however, if you're willing to break this convention then it's easy.

* Off the top of my head *

1. Fork their repo on Github
2. In your local, add a new remote to your fork; then fetch it, and push your changes up to it

    git remote add my-fork git@github...my-fork.git

vitasiku

from cx_Freeze import setup, Executable

include_files = [ 'app/templates/',
                  'app/static/',]

# Note: without 'jinja2.ext' in this list, we won't get the templates working. 

include = [ 'jinja2', 'jinja2.ext',]
flaskapp = Executable(script="run.py",
                      base="Win32GUI",

vitasiku

class ClusteringQuality:
    """
    Instances of this class implement the two measures of clustering quality discussed in the article, namely the davies
    bouldin index and the silhouette index. It also implements a number of useful helper methods.
    :param solution: the clustering solution of type Clustering
    :param minimum: the minimum distance allowable
    """

    def __init__(self, solution, minimum):
        """

vitasiku

class Clustering:
    """
    An instance of the Clustering is a solution i.e. a particular partitioning of the (heterogeneous) data set into
    homogeneous subsets. For Centroid based clustering algorithms this involves looking at each pattern and assigning
    it to it's nearest centroid. This is done by calculating the distance between each pattern and every centroid and
    selecting the one with the smallest distance. Here we use are using fractional distance with the default parameters.

    :param d: dimensionality of the input patterns
    :param k: the pre-specified number of clusters & centroids
    :param z: the patterns in the data set

vitasiku

class Clustering:

   def k_means_clustering(self, n, s=1.0):
        """
        This method performs the K-means clustering algorithm on the data for n iterations. This involves updating the
        centroids using the mean-shift heuristic n-times and reassigning the patterns to their closest centroids.
        :param n: number of iterations to complete
        :param s: the scaling factor to use when updating the centroids
        pick on which has a better solution (according to some measure of cluster quality)
        """

vitasiku

import math
import random
import csv
import numpy as np
import cProfile
import hashlib

memoization = {}

vitasiku

#!/bin/sh

# This is free and unencumbered software released into the public domain.
#
# Anyone is free to copy, modify, publish, use, compile, sell, or
# distribute this software, either in source code form or as a compiled
# binary, for any purpose, commercial or non-commercial, and by any
# means.
#
# In jurisdictions that recognize copyright laws, the author or authors