mirth

# encoding: utf-8

# Processor of Link Grammar for Russian output.
#
class LinkParser::Lexer
  # This exception raises when link grammar is invalid and Lexer
  # is unable to understand the output.
  #
  class InvalidLinkGrammar < RuntimeError
    attr_reader :input

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if __name__ == '__main__' and __package__ is None:
    from os import sys, path
    sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))

mirth

6ля
6лядь
6лять
b3ъeб
cock
cunt
e6aль
ebal
eblan
eбaл

mirth

from itertools import imap, ifilter

class map:
    def __init__(self, callable):
        self.callable = callable

    def __ror__(self, *iterable):
        return imap(self.callable, *iterable)

class filter:

mirth

#include <iostream>
#include <cstddef>
#include <tuple>
#include <utility>
#include <sstream>
#include <vector>

template <typename Tuple, typename F, std::size_t ...Indices>
void for_each_impl(Tuple&& tuple, F&& f, std::index_sequence<Indices...>) {
    using swallow = int[];

mirth

#include <iostream>
#include <memory>
#include <vector>

#include "visitor.hpp"

struct foo;
struct bar;

using visitor_t = basic_visitor<std::tuple<foo, bar>, void(int)>;

mirth

#include <functional>
#include <boost/mpl/inherit_linearly.hpp>
#include <boost/mpl/inherit.hpp>
#include <boost/mpl/vector.hpp>

template<typename TargetsVector, typename SignatureVector>
struct visit_method;

template<typename TargetType, typename ReturnType, typename ... ArgsTypes>
struct visit_method<TargetType, std::tuple<ReturnType, ArgsTypes ...>> {

mirth

import os
import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedKFold

prod_to_cat = pd.read_csv('levchik_folds/prod_to_category.csv')
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=4242)

split = skf.split(prod_to_cat.drop('category_id', axis=1), prod_to_cat['category_id'])

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import pandas as pd
from pathlib import Path

def get_ytid(x):
    name = Path(x).name
    ytid = name[: name.rfind('_')]
    return ytid

maybe_noise = pd.read_csv('Engine+noises_2020-08-24T19_34_31.csv')
maybe_healthy = pd.read_csv('Engine+healthy_2_2020-08-24T22_17_40.csv')

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class NoiseClassifier(nn.Module):
    def __init__(self):
        super().__init__()
        self.vgg = vggish(preprocess=False, postprocess=False)
        self.vgg.embeddings = nn.Sequential(
            nn.Linear(512 * 4 * 6 * WINDOW_MULTIPLIER, 256),
            nn.ReLU(True),
            nn.Linear(256, 256),
            nn.ReLU(True),
            nn.Linear(256, 256),