wassname

# from https://gist.github.com/wassname/00b94dc7eb7220c136685fce782be3a4
from io import StringIO
import pandas as pd
import numpy as np
from sklearn import metrics
import sklearn

def parse_classification_report(classification_report):
    """Parse a sklearn classification report to a dict."""
    return pd.read_fwf(

shivakar

import os
import SimpleHTTPServer
from SimpleHTTPServer import SimpleHTTPRequestHandler

class RangeHTTPRequestHandler(SimpleHTTPRequestHandler):
    """RangeHTTPRequestHandler is a SimpleHTTPRequestHandler
    with HTTP 'Range' support"""

    def send_head(self):
        """Common code for GET and HEAD commands.

wassname

# summarize model
from collections import OrderedDict
import pandas as pd

import torch
from torch import nn
from torch.autograd import Variable

class TorchSummarizeDf(object):
    def __init__(self, model, weights=False, input_shape=True, nb_trainable=False, debug=False):

wassname

"""Another way, note this one will load the whole array into memory ."""
from keras.preprocessing.image import ImageDataGenerator
import h5py
from keras.utils.io_utils import HDF5Matrix
seed=0
batch_size=32

# we create two instances with the same arguments
data_gen_args = dict( 
    rotation_range=90.,

wassname

from keras import backend as K

def jaccard_distance_loss(y_true, y_pred, smooth=100):
    """
    Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
            = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))
    
    The jaccard distance loss is usefull for unbalanced datasets. This has been
    shifted so it converges on 0 and is smoothed to avoid exploding or disapearing
    gradient.

nitrag

#!/bin/bash
#
# sudo apt-get install id3v2 ffmpeg
#
# USAGE: 
# cd /book title/
# bash ~/this_script_path.sh
# rm *.m4b (you need to manually remove the original in case something goes wrong)
#
#

ihsgnef

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

def colorize(words, color_array):
    # words is a list of words
    # color_array is an array of numbers between 0 and 1 of length equal to words
    cmap = matplotlib.cm.get_cmap('RdBu')
    template = '<span class="barcode"; style="color: black; background-color: {}">{}</span>'
    colored_string = ''

rewida17

#!/system/xbin/bash
#Based on https://github.com/termux/termux-app/issues/77
export PREFIX='/data/data/com.termux/files/usr'
export HOME='/data/data/com.termux/files/home'
export LD_LIBRARY_PATH='/data/data/com.termux/files/usr/lib'
export PATH="/data/data/com.termux/files/usr/bin:/data/data/com.termux/files/usr/bin/applets:$PATH"
export LANG='en_US.UTF-8'
export SHELL='/data/data/com.termux/files/usr/bin/bash'
export BIN='/data/data/com.termux/files/usr/bin'
export TERM=vt220

tmcw

Book Id,Title,Author,Author l-f,Additional Authors,ISBN,ISBN13,My Rating,Average Rating,Publisher,Binding,Number of Pages,Year Published,Original Publication Year,Date Read,Date Added,Bookshelves,Bookshelves with positions,Exclusive Shelf,My Review,Spoiler,Private Notes,Read Count,Recommended For,Recommended By,Owned Copies,Original Purchase Date,Original Purchase Location,Condition,Condition Description,BCID
13278990,The Housing Monster,prole.info,"prole.info, prole.info",,"=""160486530X""","=""9781604865301""",0,3.77,PM Press,Paperback,160,2012,2011,,2017/12/07,currently-reading,currently-reading (#3),currently-reading,,,,1,,,0,,,,,
7805,Pale Fire,Vladimir Nabokov,"Nabokov, Vladimir",,"=""0141185260""","=""9780141185262""",0,4.19,Penguin Books Ltd,Paperback,246,2000,1962,,2013/10/09,to-read,to-read (#26),to-read,,,,0,,,0,,,,,
34220725,Never Use Futura,Doug Thomas,"Thomas, Doug",Ellen Lupton,"=""1616895721""","=""9781616895723""",4,4.29,Princeton Architectural Press,Paperback,208,2017,,,2017/11/12,,,read,"Pr

wassname

"""
A noisy convolution 2d for pytorch
Adapted from:
- https://raw.githubusercontent.com/Scitator/Run-Skeleton-Run/master/common/modules/NoisyLinear.py
- https://github.com/pytorch/pytorch/pull/2103/files#diff-531f4c06f42260d699f43dabdf741b6d
More details can be found in the paper `Noisy Networks for Exploration`
Original: https://gist.github.com/wassname/001aff274c7c8196055fabfc06cf80c5
"""
import math