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

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 = ''

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)
#
#

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.

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

# 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):

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

# 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(

wassname

"""
Url: https://gist.github.com/wassname/1393c4a57cfcbf03641dbc31886123b8
"""
import unicodedata
import string

valid_filename_chars = "-_.() %s%s" % (string.ascii_letters, string.digits)
char_limit = 255

def clean_filename(filename, whitelist=valid_filename_chars, replace=' '):

Kaixhin

Probability notation

Note: Great refresher/glossary on probability/statistics and related topics here

Notation	Definition
X	Random variable
P(X)	Probability distribution over random variable X
X ~ P(X)	Random variable X follows (~) the probability distribution P(X) *