MJafarMashhadi

class PandasFloatFormatter:
    def __init__(self, new_format: str):
        self.new_format = new_format.format
        self.old_format = None

    def __enter__(self):
        self.old_format = pandas.options.display.float_format
        pandas.options.display.float_format = self.new_format

    def __exit__(self, exc_type, exc_val, exc_tb):

MJafarMashhadi

@tf.function
def has_nan(X):
    return tf.math.reduce_any(tf.math.is_nan(X))

@tf.function
def has_inf(X):
    return tf.math.reduce_any(tf.math.is_inf(X))

def check_nan(name):
    @tf.function

MJafarMashhadi

1. Run the unit tests of Apache commons math.

2. Use JInstrumenter to generate an execution log of the program. Make changes to the instrumenter and/or it's configurations as neccessary.

3. Feed the execution traces MINT to infer a model representing the behavior of commons math library. You will need to preprocess the execution traces to make them in the format that MINT can read. Make changes to the tool's configuration as neccessary.

MJafarMashhadi

#!/bin/sh

md5sum * 2>/dev/null | cut -c 2-3 | uniq | xargs mkdir

for f in *; do
   if [ -f $f ]; then
     mv "$f" $(md5sum "$f" | cut -c 2-3)
   fi
done

MJafarMashhadi

#!/bin/sh
#
# Usage:
#  ./android-backup.sh com.example.app [example_backup]
# 
# Outputs com.example.app.tar or if second parameter is provided, example_backup.tar
#
# Tarfile contains application private data such as database, shared preferences and internal files.
#

MJafarMashhadi

def shima_proof(string):
  if not string:
    return ''
  if string[-1] in {u'ا', u'و'}
    return u'%sی' % stirng
  
  return string