alexgorban

"""Illustration for various types of namespace scopes in TensorFlow.

> python tf_scopes.py
foo_name_scoped :
	v.name= v:0
	v2.name= foo_name_scoped/v2:0
	a.name= Variable:0
	b.name= Variable_1:0
	result_op.name= foo_name_scoped/Add:0
foo_op_scoped :

stevepowell99

#!/usr/bin/env python

# see http://socialdatablog.com/extract-pdf-annotations.html

myxkfolder="/home/steve/xk/" #you need to set this to where you want your to-dos to appear

import poppler, os.path, os, time, datetime

for root, dirs, files in os.walk('./'):
    for lpath in files:

florianhartig

library(mvtnorm)   # to draw multivariate normal outcomes
library(R2jags)    # JAGS-R interface

# function that makes distance matrix for a side*side 2D array  
dist.matrix <- function(side)
{
  row.coords <- rep(1:side, times=side)
  col.coords <- rep(1:side, each=side)
  row.col <- data.frame(row.coords, col.coords)
  D <- dist(row.col, method="euclidean", diag=TRUE, upper=TRUE)

iandanforth

#############################################################################
# Full Imports

from __future__ import division
import math
import random

"""
This is a pure Python implementation of the K-means Clustering algorithmn. The
original can be found here:

mason-stewart

[
  // Remapping of *some* SublimeREPL shortcuts
  { "keys": ["ctrl+super+s"], "command": "repl_transfer_current", "args": {"scope": "selection"}},
  { "keys": ["ctrl+shift+,", "s"], "command": "repl_transfer_current", "args": {"scope": "selection", "action":"view_write"}},
  { "keys": ["ctrl+shift+super+f"], "command": "repl_transfer_current", "args": {"scope": "file"}},
  { "keys": ["shift+ctrl+,", "f"], "command": "repl_transfer_current", "args": {"scope": "file", "action":"view_write"}},
  { "keys": ["ctrl+super+l"], "command": "repl_transfer_current", "args": {"scope": "lines"}},
  { "keys": ["shift+ctrl+,", "l"], "command": "repl_transfer_current", "args": {"scope": "lines", "action":"view_write"}},
  { "keys": ["ctrl+super+b"], "command": "repl_transfer_current", "args": {"scope": "block"}},
  { "keys": ["shift+ctrl+,", "b"], "command": "repl_transfer_current", "args": {"scope": "block", "action":"view_write"}}

fabianp

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: 

    "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer 1999

    "Learning to rank from medical imaging data." Pedregosa, Fabian, et al., 
    Machine Learning in Medical Imaging 2012.