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"""
The most atomic way to train and inference a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

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// save to windows-user directory

linters: with_defaults(object_name_linter = NULL,
                       object_length_linter(50),
                       commented_code_linter = NULL,
                       object_usage_linter = NULL,
                       line_length_linter(120),
                       cyclocomp_linter = cyclocomp_linter(50))

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# example of bar plot with individual subject points for Anya -04/24/2019
# added error bars - 04/25/2019

library(tidyverse) #will need to install this first (run: install.packages("tidyverse"))


## fake data
data = tibble( # creating a dataframe (aka "tibble") called data
  subject = rep(1:10,times = 2 ), # making a column/vector of subject numbers (1-10) x2
  condition = rep(c("hard", "easy"), each = 10), # making a column/vector of condition names

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#
# If all files excluded and you will include only specific sub-directories
# the parent path must matched before.
#

/**
!/.gitignore

###############################
# Un-ignore the affected subdirectory

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"""
PWMSearch

Searches through a fasta sequence for the relevant Position Weight Matrices 
from the Jaspar Database.

"""
from __future__ import division

from optparse import OptionParser