bkj

#!/usr/bin/env python

"""
    dump-es
"""

import json
import argparse
from elasticsearch import Elasticsearch
from elasticsearch.helpers import scan

bkj

CC=g++

CFLAGS=-fPIC -m64 -Wall -g -O3 -mavx -msse4 -mpopcnt -fopenmp -Wno-sign-compare -std=c++11 -fopenmp
LDFLAGS=-g -fPIC -fopenmp

# common linux flags
SHAREDEXT=so
SHAREDFLAGS=-shared
FAISSSHAREDFLAGS=-shared

bkj

#!/usr/bin/env python

"""
    latlon2cartesian.py
    
    !! These are parameterized differently than stuff listed in textbooks...
    
    !!!!!! gist `bkj/latlon2cartesian-new.py` has better API
"""

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import numpy as np
from snapvx import *
from cvxpy import *
import time

np.random.seed(123)

num_nodes = 2000
node_deg = 3

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from __future__ import division
import numpy as np

def mut_info(X, y):
    n = X.shape[0]
    
    n_11 = np.asarray(X[y].sum(axis=0)).squeeze()
    n_01 = np.asarray(X[~y].sum(axis=0)).squeeze()
    n_10 = np.asarray(y.sum() - n_11).squeeze()
    n_00 = np.asarray(n - n_11 - n_01 - n_10).squeeze()

bkj

import os
import re
import string
import numpy as np

from sklearn.feature_extraction.text import CountVectorizer

import torch
from torch import nn

bkj

#!/usr/bin/env python

"""
  bow2adjlist.py
  
  Convert a sparse matrix (eg term-document matrix) to two dense matrices
  
  Useful for feeding into BOW models
"""

bkj

#!/usr/bin/env python

"""
    pytorch-dask.py
"""

import torch
from torch import nn
from torch.nn import functional as F

bkj

#!/usr/bin/env python

"""
    auction-lap.py
    
    From
        https://dspace.mit.edu/bitstream/handle/1721.1/3265/P-2108-26912652.pdf;sequence=1
"""

from __future__ import print_function, division

bkj

#!/usr/bin/env python


"""
    sgdr.py
    
    Code to reproduce Fig. 1 from https://arxiv.org/pdf/1608.03983.pdf
"""

import numpy as np