jaganadhg

import tarfile

def get_file_match_patterns(tar_file_path : str, pattern : str) -> int:
    """
        Count number of file containing pattern in a tar file without extract.
        :params tar_file_path: Absolute path to tar file
        :params pattern: patterns to searh in the file names
        :returns count_matching: Count of matching files
    """
    tar_content = tarfile.open(tar_file_path)

jaganadhg

import re
from collections import Counter

data = ["/mnt/volume1/vol/img.img","/mnt/volume1/some.img","/mnt/volume2/simg.img"]

def match_volume(input_data,search_patten):
    regex_patt = re.compile(search_patten)
    macthed_gen = [regex_patt.search(inp) for inp in input_data]
    match_count = Counter(mtch.group() for mtch in macthed_gen if mtch)
    return match_count

jaganadhg

from sklearn.cluster import KMeans
from sklearn import metrics
import cv2
# By Adrian Rosebrock
import numpy as np
import cv2


# Load the image
image = cv2.imread("red.png")

jaganadhg

import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

jaganadhg

- word2vec https://arxiv.org/abs/1310.4546
- sentence2vec, paragraph2vec, doc2vec http://arxiv.org/abs/1405.4053
- tweet2vec http://arxiv.org/abs/1605.03481
- tweet2vec https://arxiv.org/abs/1607.07514
- author2vec http://dl.acm.org/citation.cfm?id=2889382
- item2vec http://arxiv.org/abs/1603.04259
- lda2vec https://arxiv.org/abs/1605.02019
- illustration2vec http://dl.acm.org/citation.cfm?id=2820907
- tag2vec http://ktsaurabh.weebly.com/uploads/3/1/7/8/31783965/distributed_representations_for_content-based_and_personalized_tag_recommendation.pdf
- category2vec http://www.anlp.jp/proceedings/annual_meeting/2015/pdf_dir/C4-3.pdf

jaganadhg

#!/usr/bin/env python
"""
Author : Jaganadh Gopinadhan
Licence : Apahce 2
e-mail jaganadhg at gmail dot com 

"""
import scipy

from sklearn.cluster import KMeans

jaganadhg

-- Conformal Prediction Tool https://github.com/donlnz/nonconformist -- Ensemble Methods in ML http://web.engr.oregonstate.edu/~tgd/publications/mcs-ensembles.pdf -- Q&A in Restriced Domains http://www.mitpressjournals.org/doi/pdf/10.1162/coli.2007.33.1.41 https://web.stanford.edu/~jurafsky/slp3/28.pdf

-- Prediction Server https://github.com/ucbrise/clipper

-- Reinforcement Learning https://webdocs.cs.ualberta.ca/~sutton/book/bookdraft2016sep.pdf

-- sklearn Model to Java https://github.com/nok/sklearn-porter

jaganadhg

%!TEX TS-program = xelatex
\documentclass[margin=0.1in]{friggeri-cv}
\usepackage{afterpage}
\usepackage{hyperref}
\usepackage{color}
\usepackage{xcolor}
\usepackage{smartdiagram}
\usepackage{fontspec}
\usepackage{float}
\usepackage{needspace}

jaganadhg

Amazon question/answer data http://jmcauley.ucsd.edu/data/amazon/qa/

Deep Learning for Visual Question Answering https://avisingh599.github.io/deeplearning/visual-qa/ 
http://cs.umd.edu/~miyyer/data/deepqa.pdf

R Clustering Packages http://www.rdocumentation.org/taskviews/Cluster 

Deep learning http://deeplearning.net/software/pylearn2/tutorial/notebook_tutorials.html#notebook-tutorials 

Cluster with Theano https://gist.github.com/erogol/7946246 

jaganadhg

from sklearn.cluster import k_means_
from sklearn.metrics.pairwise import cosine_similarity, pairwise_distances
from sklearn.preprocessing import StandardScaler


def create_cluster(sparse_data, nclust = 10):

    # Manually override euclidean
    def euc_dist(X, Y = None, Y_norm_squared = None, squared = False):
        #return pairwise_distances(X, Y, metric = 'cosine', n_jobs = 10)