Greg grej

grej / AliasSampling.ipynb

Created March 6, 2018 20:49 — forked from jph00/AliasSampling.ipynb

Fast weighted sampling using the alias method in numba

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grej / pytorch-numba.py

Created November 9, 2017 18:30 — forked from t-vi/pytorch-numba.py

	from timeit import default_timer as time
	import numpy as np
	from numba import cuda
	import os
	os.environ['NUMBAPRO_LIBDEVICE']='/usr/lib/nvidia-cuda-toolkit/libdevice/'
	os.environ['NUMBAPRO_NVVM']='/usr/lib/x86_64-linux-gnu/libnvvm.so.3.1.0'

	import numpy
	import torch
	import ctypes

grej / 0.textrank_init.py

Last active August 29, 2015 14:08 — forked from ceteri/0.textrank_init.py

	import nltk
	nltk.download()

	## use nltk.download() within a Python prompt to
	## download the `punkt` data

	## Anaconda is recommended, to pick up NumPy, NLTK, etc.
	## http://continuum.io/downloads

	## this also requires TextBlob/PerceptronTagger

grej / sklearn-lr-spark.py

Last active August 29, 2015 14:06 — forked from changbinwang/sklearn-lr-spark.py

	import sys

	from pyspark.context import SparkContext
	from numpy import array, random as np_random
	from sklearn import linear_model as lm
	from sklearn.base import copy

	N = 10000 # Number of data points
	D = 10 # Numer of dimensions
	ITERATIONS = 5

grej / sklearn-sparselr-spark-hdfs.py

Last active August 29, 2015 14:06 — forked from myui/sklearn-sparselr-spark-hdfs.py

	import sys

	from pyspark.context import SparkContext
	from numpy import array, random as np_random
	from sklearn import linear_model as lm
	from sklearn.base import copy
	from scipy import sparse as sp

	#MAX_FEATURES=1000
	MAX_FEATURES=16777216

grej / sklearn-lr-spark.py

Last active August 29, 2015 14:06 — forked from MLnick/sklearn-lr-spark.py

	import sys

	from pyspark.context import SparkContext
	from numpy import array, random as np_random
	from sklearn import linear_model as lm
	from sklearn.base import copy

	N = 10000 # Number of data points
	D = 10 # Numer of dimensions
	ITERATIONS = 5