iamaaditya

import ossaudiodev as oss
from numpy import fft
import math
import pygame
import pygame.surfarray as surfarray


d = oss.open('rw')
d.setfmt(oss.AFMT_U16_LE)
d.channels(1)

iamaaditya

# from: http://www.4dsolutions.net/cgi-bin/py2html.cgi?script=/ocn/python/primes.py

"""
 primes.py  -- Oregon Curriculum Network (OCN)
 Feb  1, 2001  changed global var primes to _primes, added relative primes test
 Dec 17, 2000  appended probable prime generating methods, plus invmod
 Dec 16, 2000  revised to use pow(), removed methods not in text, added sieve()
 Dec 12, 2000  small improvements to erastosthenes()
 Dec 10, 2000  added Euler test
 Oct  3, 2000  modified fermat test

iamaaditya

" cVim configuration -- Aaditya Prakash
" gist id -- ed0f4da609dde6b71b43

" Settings

"set noautofocus
set cncpcompletion

set smoothscroll
set nohud

iamaaditya

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Load the data, converters convert the letter to a number
data= np.loadtxt('letter-recognition.data', dtype= 'float32', delimiter = ',',
                    converters= {0: lambda ch: ord(ch)-ord('A')})

# split the data to two, 10000 each for train and test
train, test = np.vsplit(data,2)

iamaaditya

import tensorflow as tf

"""
Multi dimensional softmax,
refer to https://github.com/tensorflow/tensorflow/issues/210

compute softmax along the dimension of target
the native softmax only supports batch_size x dimension
"""
def softmax(target, axis, name=None):

iamaaditya

""" This program takes a file, and splits it into given percentage by line number, but uses
randomization to select the files

USAGE: python randomize_split.py <file_name> <split_percentage_for_test_eg_10>

@author: aaditya prakash"""

from __future__ import division
import sys
import random

iamaaditya

# author: Aaditya Prakash

# NVIDIA-SMI does not show the full command, and when it was launched and its RAM usage.
# PS does but it does but you need PIDs for that
# lsof /dev/nvidia gives PIDs but only for the user invoking it

# usage:
#        python programs_on_gpu.py

# Sample Output

iamaaditya

Keybase proof

I hereby claim:

• I am iamaaditya on github.
• I am iamaaditya (https://keybase.io/iamaaditya) on keybase.
• I have a public key whose fingerprint is 00B9 886D 6185 B18A 290A 02B8 2C21 2B82 7EC6 0287

To claim this, I am signing this object:

iamaaditya

# Code snippet to print various ML related metrics given the y_labels and probabilities of each label (output of softmax)
# Aaditya Prakash

from sklearn.metrics import f1_score, roc_auc_score, precision_score, recall_score, accuracy_score, average_precision_score, precision_recall_curve, hamming_loss

def print(y_labels, probs):

    threshold = 0.5
    macro_auc                  = roc_auc_score(y_labels, probs, average            = 'macro')
    micro_auc                  = roc_auc_score(y_labels, probs, average            = 'micro')

iamaaditya

# XLA compilation controlled by "compile_ops" option
# compile_ops=False: 4.39 sec
# compile_ops=True: 0.90 sec


import os
os.environ['CUDA_VISIBLE_DEVICES']=''
import tensorflow as tf