Kwentar

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import multivariate_normal as mvn
from keras.datasets import mnist


class BayesClassifier:
    def fit(self, X, Y):
        self.K = len(set(Y))
        self.gaussians = []

Kwentar

from keras import Model
from keras import Sequential
from keras.applications import InceptionV3
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
import numpy as np

def get_model(count_classes):
    inception_model = InceptionV3(pooling='max')  # предобученная модель

Kwentar

import gensim
from keras.preprocessing.text import text_to_word_sequence
import pymorphy2
from tqdm import tqdm

morph = pymorphy2.MorphAnalyzer()


necessary_part = {"NOUN", "ADJF", "ADJS", "VERB", "INFN", "PRTF", "PRTS", "GRND"}
with open('text.txt', 'r', encoding='utf8') as f:

Kwentar

using UnityEngine;
using System.Collections;
using Microsoft.Kinect.VisualGestureBuilder;
using Microsoft.Kinect;

public class KinectManagerScript : MonoBehaviour 
{
    VisualGestureBuilderDatabase _dbGestures;
    Windows.Kinect.KinectSensor _kinect;
    VisualGestureBuilderFrameSource _gestureFrameSource;

Kwentar

using UnityEngine;
using System.Collections;

public class MainSphereScript : MonoBehaviour {

  bool moving = false;
  int countMovingFrames = 100; //время движения в кадрах
  int currentCountFrames = 0;
  bool reverse = false; //флаг прямого/обратного движения
  void Start ()