<commit_before># -*- coding: utf-8 -*-
import logging
import sys

import numpy as np

import grayskull.agents.linear.base

log = logging.getLogger(name=__name__)


class LinearGuessing(grayskull.agents.linear.base.LinearAgent):
    def __init__(self, n_guesses=10, *args, **kwargs):
        """
        Generate `n_guesses` random settings for the model's weights and
        choose the best (where "best" is defined as the configuration that
        leads to the highest per-episode reward).

        Parameters
        ----------
        n_guesses : int, optional
            How many guesses to try
        *args, **kwargs
            Passed on to super class
        """
        super(LinearGuessing, self).__init__(*args, **kwargs)

        self.n_guesses = n_guesses

        # make n_guesses guesses
        # this will be a (n_guesses, unrolled observation_space) array
        self.weights = self.guess_weights(n_guesses)

        # track the episode
        self.episode = 0

        # track the rewards
        self.rewards = np.zeros(n_guesses, dtype=np.float)<commit_msg>Increased to 10000 guesses as directed in the OpenAI request for research<commit_after># -*- coding: utf-8 -*-
import logging
import sys

import numpy as np

import grayskull.agents.linear.base

log = logging.getLogger(name=__name__)


class LinearGuessing(grayskull.agents.linear.base.LinearAgent):
    def __init__(self, n_guesses=10000, *args, **kwargs):
        """
        Generate `n_guesses` random settings for the model's weights and
        choose the best (where "best" is defined as the configuration that
        leads to the highest per-episode reward).

        Parameters
        ----------
        n_guesses : int, optional
            How many guesses to try
        *args, **kwargs
            Passed on to super class
        """
        super(LinearGuessing, self).__init__(*args, **kwargs)

        self.n_guesses = n_guesses

        # make n_guesses guesses
        # this will be a (n_guesses, unrolled observation_space) array
        self.weights = self.guess_weights(n_guesses)

        # track the episode
        self.episode = 0

        # track the rewards
        self.rewards = np.zeros(n_guesses, dtype=np.float)
{'fraction_non_alphanumeric': 0.09143881667413715, 'fraction_numerical': 0.007171671896010758, 'mean_word_length': 2.402439024390244, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3288438', 'n_tokens_mistral': 694, 'n_tokens_neox': 635, 'n_words': 196}