andrew giessel andrewgiessel

## turbo_colormap.py
# Copyright 2019 Google LLC.
# SPDX-License-Identifier: Apache-2.0

# Author: Anton Mikhailov

turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.913

## min-char-rnn.py
"""
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)

## theano_mlp_small.py
"""A stripped-down MLP example, using Theano.

Based on the tutorial here: http://deeplearning.net/tutorial/mlp.html

This example trims away some complexities, and makes it easier to see how Theano works.

Design changes:

* Model compiled in a distinct function, so that symbolic variables are not in run-time scope.
* No classes. Network shown by chained function calls.

## tld.json
{"tlds": ["ac", "com.ac", "edu.ac", "gov.ac", "net.ac", "mil.ac", "org.ac", "ad", "nom.ad", "ae", "co.ae", "net.ae", "org.ae", "sch.ae", "ac.ae", "gov.ae", "mil.ae", "aero", "accident-investigation.aero", "accident-prevention.aero", "aerobatic.aero", "aeroclub.aero", "aerodrome.aero", "agents.aero", "aircraft.aero", "airline.aero", "airport.aero", "air-surveillance.aero", "airtraffic.aero", "air-traffic-control.aero", "ambulance.aero", "amusement.aero", "association.aero", "author.aero", "ballooning.aero", "broker.aero", "caa.aero", "cargo.aero", "catering.aero", "certification.aero", "championship.aero", "charter.aero", "civilaviation.aero", "club.aero", "conference.aero", "consultant.aero", "consulting.aero", "control.aero", "council.aero", "crew.aero", "design.aero", "dgca.aero", "educator.aero", "emergency.aero", "engine.aero", "engineer.aero", "entertainment.aero", "equipment.aero", "exchange.aero", "express.aero", "federation.aero", "flight.aero", "freight.aero", "fuel.aero", "gliding.aero", "government

## gist:6071508
import cPickle as pickle

def save(obj, filename):
    """Simple wrapper to pickle an object on disk

    :param: obj, any pickable object
    :param: filename, string representation of the file to save to

    """
    with open(filename, 'wb') as f:

## remove_output.py
"""
Usage: python remove_output.py notebook.ipynb [ > without_output.ipynb ]
Modified from remove_output by Minrk

"""
import sys
import io
import os
from IPython.nbformat.current import read, write

## grid_search.py
"""Parallel grid search for sklearn's GradientBoosting.

This script uses IPython.parallel to run cross-validated
grid search on an IPython cluster. Each cell on the parameter grid
will be evaluated ``K`` times - results are stored in MongoDB.

The procedure tunes the number of trees ``n_estimators`` by averaging
the staged scores of the GBRT model averaged over all K folds.

You need an IPython ipcluster to connect to - for local use simply

## test_gridded_array.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
*****************************************************************************
Design document for a ``gridded_array`` class
*****************************************************************************

:Author:    Andreas Hilboll <andreas@hilboll.de>
:Date:      Wed Sep 26 14:48:57 2012

## 00-about.md

      
              19 files
            
          
              10 forks
            
          
              9 comments
            
          
              52 stars
            
          
                jasonrudolph
                / 00-about.md
            
            
              Created
              September 21, 2012 18:42
            
              
                Rough Notes from Strange Loop 2012
              
          
    About

This gist is a collection of my rough notes from Strange Loop 2012.
@jasonrudolph on Twitter
|
@jasonrudolph on GitHub
Did I Mention That They're Rough?


## gist:2491663
from itertools import chain

def parse_range(rng):
    parts = rng.split('-')
    if 1 > len(parts) > 2:
        raise ValueError("Bad range: '%s'" % (rng,))
    parts = [int(i) for i in parts]
    start = parts[0]
    end = start if len(parts) == 1 else parts[1]
    if start > end:
	# Copyright 2019 Google LLC.
	# SPDX-License-Identifier: Apache-2.0

	# Author: Anton Mikhailov

	turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.913
	"""
	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)
	"""A stripped-down MLP example, using Theano.

	Based on the tutorial here: http://deeplearning.net/tutorial/mlp.html

	This example trims away some complexities, and makes it easier to see how Theano works.

	Design changes:

	* Model compiled in a distinct function, so that symbolic variables are not in run-time scope.
	* No classes. Network shown by chained function calls.
	import cPickle as pickle

	def save(obj, filename):
	"""Simple wrapper to pickle an object on disk

	:param: obj, any pickable object
	:param: filename, string representation of the file to save to

	"""
	with open(filename, 'wb') as f:
	"""
	Usage: python remove_output.py notebook.ipynb [ > without_output.ipynb ]
	Modified from remove_output by Minrk

	"""
	import sys
	import io
	import os
	from IPython.nbformat.current import read, write
	"""Parallel grid search for sklearn's GradientBoosting.

	This script uses IPython.parallel to run cross-validated
	grid search on an IPython cluster. Each cell on the parameter grid
	will be evaluated ``K`` times - results are stored in MongoDB.

	The procedure tunes the number of trees ``n_estimators`` by averaging
	the staged scores of the GBRT model averaged over all K folds.

	You need an IPython ipcluster to connect to - for local use simply
	#!/usr/bin/env python
	# -- coding: utf-8 --
	"""
	*****************************************************************************
	Design document for a ``gridded_array`` class
	*****************************************************************************

	:Author: Andreas Hilboll <andreas@hilboll.de>
	:Date: Wed Sep 26 14:48:57 2012
	from itertools import chain

	def parse_range(rng):
	parts = rng.split('-')
	if 1 > len(parts) > 2:
	raise ValueError("Bad range: '%s'" % (rng,))
	parts = [int(i) for i in parts]
	start = parts[0]
	end = start if len(parts) == 1 else parts[1]
	if start > end: