Nick Amin aminnj

## capacity.py
import numpy as np
# number of nodes per layer
nodes = np.array([2,3,4])
# upper bound on capacity according to eq 1.1 of https://arxiv.org/pdf/1901.00434.pdf
# pairwise product of nodes weighted by the minimum layer width up to that point
capacity = np.sum(np.minimum.accumulate(nodes)[:-1]*nodes[:-1]*nodes[1:])
print(capacity)

# a fully connected arch with equal number of nodes per layer will have a capacity
# that scales linearly with the total matrix multiplication time (is this true?)

## character_widths.html
<html>
    <head>
        <style>
body {
    font: small/1.5 Arial,Helvetica,sans-serif;
    letter-spacing: normal;
}
        </style>
        <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script>
        <script>

## parse_takeout.py
import json
import numpy as np
import pandas as pd
pd.set_option('display.width', None)

def get_dfs(which=["android","assistant","chrome","gmail","playstore","image","maps","search","voice","location"]):
    dfs = {}
    def get_fname(name):
        if "Location History" in name:
            return "Takeout/Location History/Location History.json"

## excel.py
import os
import numpy as np

import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt

# matplotlib.font_manager._rebuild() # after installing calibri
from matplotlib import rcParams
from cycler import cycler

## install_gpuxgboost.sh
# main instructions from https://xgboost.readthedocs.io/en/latest/build.html#building-with-gpu-support

# first install cmake somewhere
curl -O -L https://cmake.org/files/v3.12/cmake-3.12.0-Linux-x86_64.tar.gz
tar xf cmake*.tar.gz
cd cmake*/
export CMAKE_ROOT=`pwd`

# get 10X environment for slc7
cd /cvmfs/cms.cern.ch/slc7_amd64_gcc630/cms/cmssw/CMSSW_10_2_0_pre6/ ; cmsenv ; cd -

## xgboost_dump.py
# in some versions of xgboost, can't easily dump to json, so dump to the "plaintext"
# format, and then do some string parsing to make a dictionary and then json
# next, convert the json to a c++ function (nested ternary statements for the tree branching)
# and don't forget the sigmoid at the end to get probabilities!

# let's say you have
# ...
# bst = xgb.train( param.items(), dtrain, num_round, evallist, early_stopping_rounds=20 )
# feature_names = ["njets", "nbtags", "met"]

## dygan.ipynb

      
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                aminnj
                / dygan.ipynb
            
            
              Created
              September 18, 2018 22:10
            
              
                dygan
              
          
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## UserTarball.py
from __future__ import print_function

"""
stolen from https://github.com/dmwm/CRABClient/blob/master/src/python/CRABClient/JobType/UserTarball.py
"""

import os
import glob
import tarfile
import tempfile

## test_awkward.py
import sys
import time
import numba
import numpy as np
import awkward
from awkward import JaggedArray

@numba.jit(nopython=True,cache=True)
def numba_min(content,offsets):
    result = np.zeros(len(offsets)-1,dtype=content.dtype)

## gan_mnist_experiment.ipynb

      
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                aminnj
                / gan_mnist_experiment.ipynb
            
            
              Created
              August 2, 2018 21:48
            
          
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	import numpy as np
	# number of nodes per layer
	nodes = np.array([2,3,4])
	# upper bound on capacity according to eq 1.1 of https://arxiv.org/pdf/1901.00434.pdf
	# pairwise product of nodes weighted by the minimum layer width up to that point
	capacity = np.sum(np.minimum.accumulate(nodes)[:-1]nodes[:-1]nodes[1:])
	print(capacity)

	# a fully connected arch with equal number of nodes per layer will have a capacity
	# that scales linearly with the total matrix multiplication time (is this true?)
	<html>
	<head>
	<style>
	body {
	font: small/1.5 Arial,Helvetica,sans-serif;
	letter-spacing: normal;
	}
	</style>
	<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script>
	<script>
	import json
	import numpy as np
	import pandas as pd
	pd.set_option('display.width', None)

	def get_dfs(which=["android","assistant","chrome","gmail","playstore","image","maps","search","voice","location"]):
	dfs = {}
	def get_fname(name):
	if "Location History" in name:
	return "Takeout/Location History/Location History.json"
	import os
	import numpy as np

	import matplotlib as mpl
	mpl.use('Agg')
	import matplotlib.pyplot as plt

	# matplotlib.font_manager._rebuild() # after installing calibri
	from matplotlib import rcParams
	from cycler import cycler
	# main instructions from https://xgboost.readthedocs.io/en/latest/build.html#building-with-gpu-support

	# first install cmake somewhere
	curl -O -L https://cmake.org/files/v3.12/cmake-3.12.0-Linux-x86_64.tar.gz
	tar xf cmake*.tar.gz
	cd cmake*/
	export CMAKE_ROOT=`pwd`

	# get 10X environment for slc7
	cd /cvmfs/cms.cern.ch/slc7_amd64_gcc630/cms/cmssw/CMSSW_10_2_0_pre6/ ; cmsenv ; cd -
	# in some versions of xgboost, can't easily dump to json, so dump to the "plaintext"
	# format, and then do some string parsing to make a dictionary and then json
	# next, convert the json to a c++ function (nested ternary statements for the tree branching)
	# and don't forget the sigmoid at the end to get probabilities!

	# let's say you have
	# ...
	# bst = xgb.train( param.items(), dtrain, num_round, evallist, early_stopping_rounds=20 )
	# feature_names = ["njets", "nbtags", "met"]
	from __future__ import print_function

	"""
	stolen from https://github.com/dmwm/CRABClient/blob/master/src/python/CRABClient/JobType/UserTarball.py
	"""

	import os
	import glob
	import tarfile
	import tempfile
	import sys
	import time
	import numba
	import numpy as np
	import awkward
	from awkward import JaggedArray

	@numba.jit(nopython=True,cache=True)
	def numba_min(content,offsets):
	result = np.zeros(len(offsets)-1,dtype=content.dtype)