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Jaganadh Gopinadhan jaganadhg

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jaganadhg /
Last active May 9, 2020
Tar File Explororo
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 =
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 = [ for inp in input_data]
match_count = Counter( for mtch in macthed_gen if mtch)
return match_count
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 /
Created May 18, 2017 — forked from chsasank/
Elastic transformation of an image in Python
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
View something2vec.list
- word2vec
- sentence2vec, paragraph2vec, doc2vec
- tweet2vec
- tweet2vec
- author2vec
- item2vec
- lda2vec
- illustration2vec
- tag2vec
- category2vec
jaganadhg / gap
Last active Feb 11, 2018
Implementation of Gap Statistic from Tibshirani, Walther, Hastie to determine the inherent number of clusters in a dataset with k-means clustering.
View gap
#!/usr/bin/env python
Author : Jaganadh Gopinadhan
Licence : Apahce 2
e-mail jaganadhg at gmail dot com
import scipy
from sklearn.cluster import KMeans
View friggericvjagan.tex
%!TEX TS-program = xelatex
jaganadhg / MSSS_BMS.txt
Last active Jul 1, 2016
View MSSS_BMS.txt
Amazon question/answer data
Deep Learning for Visual Question Answering
R Clustering Packages
Deep learning
Cluster with Theano
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)
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