00_DanceVision_AI-driven_Dance_Proficiency_Assessment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1c3ccdab",
   "metadata": {},
   "source": [
    "## DanceVision – AI-driven Dance Proficiency Assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b02740b",
   "metadata": {},
   "source": [
    "### Contents:\n",
    "- [Background](#1-Background)\n",
    "- [Data Collection and Exploration](#2-Data-Collection-and-Exploration)\n",
    "- [Modeling](#3-Modeling)\n",
    "- [Key Insights & Recommendations](#3-Conclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64237a59",
   "metadata": {},
   "source": [
    "## 1 Background"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54addd42",
   "metadata": {},
   "source": [
    "Dance is a universal language that transcends borders and cultures. It is not only a form of entertainment but also a medium for self-expression and communication. With the advent of technology, the way we perceive and interact with dance has evolved. The project at hand aims to leverage the power of machine learning and MediaPipe to detect and analyze dance poses, thereby determining the proficiency of a dancer. This technology can be utilized in various ways, such as an AI dance trainer, a tool for auditions, talent scouting, and entertainment.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7c9a76",
   "metadata": {},
   "source": [
    "#### Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49198755",
   "metadata": {},
   "source": [
    " There is a need for a more efficient, objective, and accessible method of evaluating dance performance. This project aims to address these challenges by developing a machine learning model that can accurately detect and analyze dance poses. The model will compare a dancer's poses with those of a professional dancer, providing an objective assessment of their performance. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2fb9c6",
   "metadata": {},
   "source": [
    "#### Research"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ab3c53",
   "metadata": {},
   "source": [
    "The online learning market has been rapidly expanding; in 2019, it was a USD 144 B industry, and by 2026, is expected to reach USD 374.3 B [1](#1) The impact of COVID-19 on dance education has been significant, with many institutions forced to close their doors and move their classes online. This has led to a surge in interest in online dance classes and the use of technology in dance education. However, the transition to online learning has also highlighted the limitations of current approaches, particularly in terms of providing effective feedback and supporting independent practice.\n",
    "\n",
    "(In the online environment, it is especially challenging to learn body activities, such as dancing or playing sports. For example, watching and following videos on YouTube provide convenient and easy opportunities to learn dance for beginners. However, the interactions between students and teachers are limited in this environment. Unlike offline dance classes where teachers and students are co-located simultaneously, beginners may not easily grasp visual cues in tutorial videos, and compare their movements with those in the videos.)\n",
    "\n",
    "Moreover, the global dance and electronic music market revenue was estimated to be around 7.1 billion U.S. dollars in 2019 [2](#2), indicating a substantial market for this technology. Specifically, in South Korea, the K-pop industry is known for its rigorous dance routines and synchronization between band members. It is commonly used as a metrics in auditions as well. However, the time-consuming nature of traditional auditions may be taking up resources for entertainment companies and also applicants. These auditions often require significant travel and preparation time, making them inaccessible for many potential students. Additionally, the audition process can be highly subjective, with decisions often based on a single performance. These issues may be alleviated with the use of AI dance assessment. Talent scouts can use it to scan through numerous online videos efficiently as well in search of suitable candidates.\n",
    "\n",
    "Recent years have seen the growth of AI technologies in various domains, including dance. These technologies, such as human pose estimation models for 2D video, allow us to recognize dance movements and offer feedback to learners based on the captured movements. In addition, AI-based pose estimation is more lightweight and practical than other technologies for motion recognition, such as motion capture and Kinect, which are expensive, and have limited accessibility to being generally applied for students. Hence, this project aims to provide a light weight, accessible, one-of-its-kind time-specific AI dance trainer for all to use.\n",
    "\n",
    "References:\n",
    "<br>1. Facts & Factors. (2020). E-Learning Market. \n",
    "<br>2. Global dance and electronic music market revenue. Link: https://www.statista.com/statistics/270229/usage-duration-of-social-networks-by-country/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb57c76",
   "metadata": {},
   "source": [
    "#### Method"
   ]
  },
  {
   "attachments": {
    "Method.jpg": {
     "image/jpeg": "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"
    }
   },
   "cell_type": "markdown",
   "id": "4f25d37e",
   "metadata": {},
   "source": [
    "![Method.jpg](attachment:Method.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d3a877f",
   "metadata": {},
   "source": [
    "**Pose Detection**\n",
    "\n",
    "MediaPipe is an open-source framework developed by Google for building multimodal (video, audio, and sensor) applied machine learning pipelines.\n",
    "\n",
    "In this project, we will be using their pose estimation solution for detecting key landmarks on the human body. These solutions utilize pre-trained models that have been trained on large datasets, and they can be used out-of-the-box to make predictions on new data - to track poses.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f8b6de",
   "metadata": {},
   "source": [
    "Given any visible body, MediaPipe library detects 33 different landmarks on the body and returns the X,Y,Z location in the image for each of the landmarks below.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0627e119",
   "metadata": {},
   "source": [
    "![PoseLandMarks](./images/pose_landmarks.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c428c728",
   "metadata": {},
   "source": [
    "Here, I extracted 12 landmarks with only x and y values to reduce noise. A pose label will then be assigned to each set of pose values. This will then be passed into the ML models for training.\n",
    "\n",
    "<b>Right Arm</b>\n",
    "Landmarks:  12, 14, 16\n",
    "\n",
    "<b>Left Arm</b>\n",
    "Landmarks: 11, 13, 15\n",
    "\n",
    "<b>Right Leg</b>\n",
    "Landmarks:  24, 26, 28\n",
    "\n",
    "<b>Left Leg</b>\n",
    "Landmarks: 23, 25, 27"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd2d521",
   "metadata": {},
   "source": [
    "## 2 Data Collection and Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6304efec",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "**Data collection: 01_pose_training_data.py**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ccbafa8",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`01_pose_training_data.py` captures data at one timeframe of a dance choreography video which are labelled as one pose. \n",
    "\n",
    "In the song \"Super Shy\", 10 poses across the 27 seconds choreography video were selected. To ensure that we get enough data for each pose, all information within an range of about 5-20 frames are collected, and this is done over 4 videos of the same dance choregraphy by other members of the same KPop group.  \n",
    "\n",
    "Usage: \n",
    "\n",
    "python 01_training_data.py --class-name Hit --video-folder vid/raw --start-frame 38 --end-frame 56 --file-name pose_training_12pt.csv --select-random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "532c5e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1a9d7c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_name</th>\n",
       "      <th>start_frame</th>\n",
       "      <th>end_frame</th>\n",
       "      <th>file_name</th>\n",
       "      <th>model_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L-Up</td>\n",
       "      <td>38</td>\n",
       "      <td>56</td>\n",
       "      <td>training1.csv</td>\n",
       "      <td>model1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L-Up</td>\n",
       "      <td>84</td>\n",
       "      <td>97</td>\n",
       "      <td>training1.csv</td>\n",
       "      <td>model1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L-Up</td>\n",
       "      <td>420</td>\n",
       "      <td>434</td>\n",
       "      <td>training1.csv</td>\n",
       "      <td>model1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L-Up</td>\n",
       "      <td>467</td>\n",
       "      <td>483</td>\n",
       "      <td>training1.csv</td>\n",
       "      <td>model1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L-Down</td>\n",
       "      <td>69</td>\n",
       "      <td>77</td>\n",
       "      <td>training2.csv</td>\n",
       "      <td>model2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  class_name  start_frame  end_frame      file_name model_name\n",
       "0       L-Up           38         56  training1.csv     model1\n",
       "1       L-Up           84         97  training1.csv     model1\n",
       "2       L-Up          420        434  training1.csv     model1\n",
       "3       L-Up          467        483  training1.csv     model1\n",
       "4     L-Down           69         77  training2.csv     model2"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47e6bf71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jinji\\Desktop\\GA_J\\DSI-SG-37 - Copy\\Capstone Project\\dance_c3\n",
      "Class name:  L-Up  File saved: training1.csv\n",
      "Class name:  L-Up  File saved: training1.csv\n",
      "Class name:  L-Up  File saved: training1.csv\n",
      "Class name:  L-Up  File saved: training1.csv\n",
      "Class name:  L-Down  File saved: training2.csv\n",
      "Class name:  L-Down  File saved: training2.csv\n",
      "Class name:  L-Down  File saved: training2.csv\n",
      "Class name:  L-Down  File saved: training2.csv\n",
      "Class name:  L-Extend  File saved: training3.csv\n",
      "Class name:  L-Extend  File saved: training3.csv\n",
      "Class name:  L-Keep  File saved: training4.csv\n",
      "Class name:  L-Keep  File saved: training4.csv\n",
      "Class name:  L-Keep  File saved: training4.csv\n",
      "Class name:  R-Extend  File saved: training5.csv\n",
      "Class name:  R-Extend  File saved: training5.csv\n",
      "Class name:  R-Keep  File saved: training6.csv\n",
      "Class name:  R-Keep  File saved: training6.csv\n",
      "Class name:  R-Up  File saved: training7.csv\n",
      "Class name:  R-Up  File saved: training7.csv\n",
      "Class name:  R-Up  File saved: training7.csv\n",
      "Class name:  R-Up  File saved: training7.csv\n",
      "Class name:  R-Down  File saved: training8.csv\n",
      "Class name:  R-Down  File saved: training8.csv\n",
      "Class name:  R-Down  File saved: training8.csv\n",
      "Class name:  R-Down  File saved: training8.csv\n",
      "Class name:  L-Fold  File saved: training9.csv\n",
      "Class name:  L-Fold  File saved: training9.csv\n",
      "Class name:  L-Fold  File saved: training9.csv\n",
      "Class name:  L-Fold  File saved: training9.csv\n",
      "Class name:  L-Fold  File saved: training9.csv\n",
      "Class name:  L-Fold  File saved: training9.csv\n",
      "Class name:  R-Fold  File saved: training10.csv\n",
      "Class name:  R-Fold  File saved: training10.csv\n",
      "Class name:  R-Fold  File saved: training10.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import subprocess\n",
    "import os\n",
    "\n",
    "def run_script(script, args=[]):\n",
    "    subprocess.run([\"python\", script] + args, check=True)\n",
    "\n",
    "df = pd.read_excel('data/key_poses.xlsx')\n",
    "print(os.getcwd())\n",
    "\n",
    "# Iterate through the rows of the DataFrame\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    class_name = row['class_name']\n",
    "    start_frame = row['start_frame']\n",
    "    end_frame = row['end_frame']\n",
    "    file_name = row['file_name']\n",
    "    print('Class name: ' ,class_name, ' File saved:', file_name)\n",
    "\n",
    "    args = ['--video-folder', str('vid/raw'),\n",
    "        \"--class-name\", str(class_name), \n",
    "        \"--start-frame\", str(start_frame), \n",
    "        \"--end-frame\", str(end_frame), \n",
    "        \"--file-name\", str(file_name), \n",
    "        \"--select-random\"]\n",
    "    \n",
    "    # Run the script\n",
    "    run_script(\"01_training_data.py\", args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d03277c4",
   "metadata": {},
   "source": [
    "**Review Pose Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99203f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7a6c45ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/supershy_training_12pt.csv', header=None)\n",
    "# Changing columns name with index number\n",
    "df = df.rename(columns={\n",
    "    df.columns[0]: 'target',\n",
    "    1:'p11x',\n",
    "    2:'p11y',\n",
    "    3:'p12x',\n",
    "    4:'p12y',\n",
    "    5:'p13x',\n",
    "    6:'p13y',\n",
    "    7:'p14x',\n",
    "    8:'p14y',\n",
    "    9:'p15x',\n",
    "    10:'p15y',\n",
    "    11:'p16x',\n",
    "    12:'p16y',\n",
    "    13:'p23x',\n",
    "    14:'p23y',\n",
    "    15:'p24x',\n",
    "    16:'p24y',\n",
    "    17:'p25x',\n",
    "    18:'p25y',\n",
    "    19:'p26x',\n",
    "    20:'p26y',\n",
    "    21:'p27x',\n",
    "    22:'p27y',\n",
    "    23:'p28x',\n",
    "    24:'p28y'\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb4817d5",
   "metadata": {},
   "source": [
    "## Raw Collected Pose Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bd00c5d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(405, 25)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "da1613ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>p11x</th>\n",
       "      <th>p11y</th>\n",
       "      <th>p12x</th>\n",
       "      <th>p12y</th>\n",
       "      <th>p13x</th>\n",
       "      <th>p13y</th>\n",
       "      <th>p14x</th>\n",
       "      <th>p14y</th>\n",
       "      <th>p15x</th>\n",
       "      <th>...</th>\n",
       "      <th>p24x</th>\n",
       "      <th>p24y</th>\n",
       "      <th>p25x</th>\n",
       "      <th>p25y</th>\n",
       "      <th>p26x</th>\n",
       "      <th>p26y</th>\n",
       "      <th>p27x</th>\n",
       "      <th>p27y</th>\n",
       "      <th>p28x</th>\n",
       "      <th>p28y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.503050</td>\n",
       "      <td>0.419582</td>\n",
       "      <td>0.416040</td>\n",
       "      <td>0.414611</td>\n",
       "      <td>0.581769</td>\n",
       "      <td>0.377122</td>\n",
       "      <td>0.565155</td>\n",
       "      <td>0.366704</td>\n",
       "      <td>0.658263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.412153</td>\n",
       "      <td>0.596647</td>\n",
       "      <td>0.543139</td>\n",
       "      <td>0.718073</td>\n",
       "      <td>0.350387</td>\n",
       "      <td>0.737964</td>\n",
       "      <td>0.573987</td>\n",
       "      <td>0.830337</td>\n",
       "      <td>0.275130</td>\n",
       "      <td>0.841092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.533189</td>\n",
       "      <td>0.442137</td>\n",
       "      <td>0.452344</td>\n",
       "      <td>0.445124</td>\n",
       "      <td>0.639032</td>\n",
       "      <td>0.400441</td>\n",
       "      <td>0.584126</td>\n",
       "      <td>0.406648</td>\n",
       "      <td>0.642906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.453835</td>\n",
       "      <td>0.597352</td>\n",
       "      <td>0.594969</td>\n",
       "      <td>0.706535</td>\n",
       "      <td>0.414527</td>\n",
       "      <td>0.718787</td>\n",
       "      <td>0.597082</td>\n",
       "      <td>0.812932</td>\n",
       "      <td>0.369997</td>\n",
       "      <td>0.823068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.516810</td>\n",
       "      <td>0.459327</td>\n",
       "      <td>0.505952</td>\n",
       "      <td>0.444943</td>\n",
       "      <td>0.540951</td>\n",
       "      <td>0.515194</td>\n",
       "      <td>0.588879</td>\n",
       "      <td>0.383308</td>\n",
       "      <td>0.580958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479574</td>\n",
       "      <td>0.597164</td>\n",
       "      <td>0.541575</td>\n",
       "      <td>0.733155</td>\n",
       "      <td>0.412311</td>\n",
       "      <td>0.724134</td>\n",
       "      <td>0.550465</td>\n",
       "      <td>0.845990</td>\n",
       "      <td>0.297138</td>\n",
       "      <td>0.833496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.515192</td>\n",
       "      <td>0.458072</td>\n",
       "      <td>0.444420</td>\n",
       "      <td>0.440142</td>\n",
       "      <td>0.579330</td>\n",
       "      <td>0.398837</td>\n",
       "      <td>0.556529</td>\n",
       "      <td>0.383716</td>\n",
       "      <td>0.629344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487831</td>\n",
       "      <td>0.619705</td>\n",
       "      <td>0.583745</td>\n",
       "      <td>0.757820</td>\n",
       "      <td>0.450874</td>\n",
       "      <td>0.757913</td>\n",
       "      <td>0.594270</td>\n",
       "      <td>0.879533</td>\n",
       "      <td>0.362966</td>\n",
       "      <td>0.877422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.529120</td>\n",
       "      <td>0.417938</td>\n",
       "      <td>0.443346</td>\n",
       "      <td>0.415669</td>\n",
       "      <td>0.440571</td>\n",
       "      <td>0.371223</td>\n",
       "      <td>0.359429</td>\n",
       "      <td>0.354078</td>\n",
       "      <td>0.337499</td>\n",
       "      <td>...</td>\n",
       "      <td>0.423327</td>\n",
       "      <td>0.581829</td>\n",
       "      <td>0.554277</td>\n",
       "      <td>0.719643</td>\n",
       "      <td>0.301146</td>\n",
       "      <td>0.678487</td>\n",
       "      <td>0.598363</td>\n",
       "      <td>0.818809</td>\n",
       "      <td>0.327419</td>\n",
       "      <td>0.792805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.525569</td>\n",
       "      <td>0.404123</td>\n",
       "      <td>0.495007</td>\n",
       "      <td>0.397034</td>\n",
       "      <td>0.593262</td>\n",
       "      <td>0.416326</td>\n",
       "      <td>0.614458</td>\n",
       "      <td>0.337485</td>\n",
       "      <td>0.615729</td>\n",
       "      <td>...</td>\n",
       "      <td>0.436914</td>\n",
       "      <td>0.563337</td>\n",
       "      <td>0.515209</td>\n",
       "      <td>0.699623</td>\n",
       "      <td>0.355171</td>\n",
       "      <td>0.701722</td>\n",
       "      <td>0.547543</td>\n",
       "      <td>0.810886</td>\n",
       "      <td>0.247410</td>\n",
       "      <td>0.815225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.483170</td>\n",
       "      <td>0.457052</td>\n",
       "      <td>0.430825</td>\n",
       "      <td>0.445130</td>\n",
       "      <td>0.501993</td>\n",
       "      <td>0.532554</td>\n",
       "      <td>0.560279</td>\n",
       "      <td>0.408881</td>\n",
       "      <td>0.569739</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466654</td>\n",
       "      <td>0.621661</td>\n",
       "      <td>0.562688</td>\n",
       "      <td>0.740640</td>\n",
       "      <td>0.432828</td>\n",
       "      <td>0.750742</td>\n",
       "      <td>0.549645</td>\n",
       "      <td>0.856688</td>\n",
       "      <td>0.316652</td>\n",
       "      <td>0.855822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.519636</td>\n",
       "      <td>0.404974</td>\n",
       "      <td>0.489878</td>\n",
       "      <td>0.399595</td>\n",
       "      <td>0.601693</td>\n",
       "      <td>0.360763</td>\n",
       "      <td>0.599202</td>\n",
       "      <td>0.348494</td>\n",
       "      <td>0.667281</td>\n",
       "      <td>...</td>\n",
       "      <td>0.430535</td>\n",
       "      <td>0.568880</td>\n",
       "      <td>0.517743</td>\n",
       "      <td>0.697846</td>\n",
       "      <td>0.338478</td>\n",
       "      <td>0.698510</td>\n",
       "      <td>0.547251</td>\n",
       "      <td>0.808285</td>\n",
       "      <td>0.244911</td>\n",
       "      <td>0.812840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.484219</td>\n",
       "      <td>0.403663</td>\n",
       "      <td>0.383849</td>\n",
       "      <td>0.408965</td>\n",
       "      <td>0.553994</td>\n",
       "      <td>0.362381</td>\n",
       "      <td>0.478277</td>\n",
       "      <td>0.369116</td>\n",
       "      <td>0.601451</td>\n",
       "      <td>...</td>\n",
       "      <td>0.379969</td>\n",
       "      <td>0.581254</td>\n",
       "      <td>0.546081</td>\n",
       "      <td>0.715429</td>\n",
       "      <td>0.348234</td>\n",
       "      <td>0.728652</td>\n",
       "      <td>0.565200</td>\n",
       "      <td>0.823092</td>\n",
       "      <td>0.283234</td>\n",
       "      <td>0.839835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.402432</td>\n",
       "      <td>0.436768</td>\n",
       "      <td>0.269835</td>\n",
       "      <td>0.455657</td>\n",
       "      <td>0.493315</td>\n",
       "      <td>0.367383</td>\n",
       "      <td>0.129527</td>\n",
       "      <td>0.508854</td>\n",
       "      <td>0.576681</td>\n",
       "      <td>...</td>\n",
       "      <td>0.321310</td>\n",
       "      <td>0.609169</td>\n",
       "      <td>0.565974</td>\n",
       "      <td>0.709806</td>\n",
       "      <td>0.318391</td>\n",
       "      <td>0.742675</td>\n",
       "      <td>0.560715</td>\n",
       "      <td>0.830472</td>\n",
       "      <td>0.284802</td>\n",
       "      <td>0.836831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.520583</td>\n",
       "      <td>0.405997</td>\n",
       "      <td>0.446742</td>\n",
       "      <td>0.400851</td>\n",
       "      <td>0.606235</td>\n",
       "      <td>0.359410</td>\n",
       "      <td>0.573203</td>\n",
       "      <td>0.354532</td>\n",
       "      <td>0.661629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.426603</td>\n",
       "      <td>0.573568</td>\n",
       "      <td>0.527259</td>\n",
       "      <td>0.697328</td>\n",
       "      <td>0.345836</td>\n",
       "      <td>0.699722</td>\n",
       "      <td>0.548848</td>\n",
       "      <td>0.800270</td>\n",
       "      <td>0.256637</td>\n",
       "      <td>0.809263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.495142</td>\n",
       "      <td>0.436183</td>\n",
       "      <td>0.418405</td>\n",
       "      <td>0.427938</td>\n",
       "      <td>0.548523</td>\n",
       "      <td>0.370330</td>\n",
       "      <td>0.531161</td>\n",
       "      <td>0.366521</td>\n",
       "      <td>0.620191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.441399</td>\n",
       "      <td>0.610268</td>\n",
       "      <td>0.544821</td>\n",
       "      <td>0.736427</td>\n",
       "      <td>0.380078</td>\n",
       "      <td>0.740564</td>\n",
       "      <td>0.583659</td>\n",
       "      <td>0.852626</td>\n",
       "      <td>0.297847</td>\n",
       "      <td>0.856207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.621405</td>\n",
       "      <td>0.454633</td>\n",
       "      <td>0.494383</td>\n",
       "      <td>0.452022</td>\n",
       "      <td>0.711118</td>\n",
       "      <td>0.410144</td>\n",
       "      <td>0.524798</td>\n",
       "      <td>0.392989</td>\n",
       "      <td>0.790904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487186</td>\n",
       "      <td>0.602513</td>\n",
       "      <td>0.609002</td>\n",
       "      <td>0.724780</td>\n",
       "      <td>0.439951</td>\n",
       "      <td>0.721126</td>\n",
       "      <td>0.591701</td>\n",
       "      <td>0.825236</td>\n",
       "      <td>0.370176</td>\n",
       "      <td>0.815689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.618353</td>\n",
       "      <td>0.445937</td>\n",
       "      <td>0.440438</td>\n",
       "      <td>0.441000</td>\n",
       "      <td>0.766636</td>\n",
       "      <td>0.472223</td>\n",
       "      <td>0.339824</td>\n",
       "      <td>0.415115</td>\n",
       "      <td>0.682432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.468068</td>\n",
       "      <td>0.601234</td>\n",
       "      <td>0.615019</td>\n",
       "      <td>0.741662</td>\n",
       "      <td>0.403197</td>\n",
       "      <td>0.729376</td>\n",
       "      <td>0.610431</td>\n",
       "      <td>0.836238</td>\n",
       "      <td>0.361827</td>\n",
       "      <td>0.829657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.485779</td>\n",
       "      <td>0.412876</td>\n",
       "      <td>0.451573</td>\n",
       "      <td>0.413236</td>\n",
       "      <td>0.496228</td>\n",
       "      <td>0.449266</td>\n",
       "      <td>0.584102</td>\n",
       "      <td>0.360402</td>\n",
       "      <td>0.522457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.406414</td>\n",
       "      <td>0.585930</td>\n",
       "      <td>0.530706</td>\n",
       "      <td>0.705357</td>\n",
       "      <td>0.346933</td>\n",
       "      <td>0.718999</td>\n",
       "      <td>0.563956</td>\n",
       "      <td>0.814286</td>\n",
       "      <td>0.287510</td>\n",
       "      <td>0.831913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.521759</td>\n",
       "      <td>0.465703</td>\n",
       "      <td>0.443643</td>\n",
       "      <td>0.440022</td>\n",
       "      <td>0.562018</td>\n",
       "      <td>0.517918</td>\n",
       "      <td>0.558337</td>\n",
       "      <td>0.386123</td>\n",
       "      <td>0.600624</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487938</td>\n",
       "      <td>0.623199</td>\n",
       "      <td>0.584935</td>\n",
       "      <td>0.757003</td>\n",
       "      <td>0.462113</td>\n",
       "      <td>0.758831</td>\n",
       "      <td>0.605657</td>\n",
       "      <td>0.880212</td>\n",
       "      <td>0.368238</td>\n",
       "      <td>0.876734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.528646</td>\n",
       "      <td>0.399928</td>\n",
       "      <td>0.487034</td>\n",
       "      <td>0.394961</td>\n",
       "      <td>0.588861</td>\n",
       "      <td>0.404374</td>\n",
       "      <td>0.622716</td>\n",
       "      <td>0.340616</td>\n",
       "      <td>0.650569</td>\n",
       "      <td>...</td>\n",
       "      <td>0.422532</td>\n",
       "      <td>0.572516</td>\n",
       "      <td>0.518922</td>\n",
       "      <td>0.705704</td>\n",
       "      <td>0.397424</td>\n",
       "      <td>0.714909</td>\n",
       "      <td>0.543822</td>\n",
       "      <td>0.813635</td>\n",
       "      <td>0.257528</td>\n",
       "      <td>0.812281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.527413</td>\n",
       "      <td>0.426842</td>\n",
       "      <td>0.468413</td>\n",
       "      <td>0.419309</td>\n",
       "      <td>0.592715</td>\n",
       "      <td>0.378062</td>\n",
       "      <td>0.583697</td>\n",
       "      <td>0.370346</td>\n",
       "      <td>0.664180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.484469</td>\n",
       "      <td>0.576140</td>\n",
       "      <td>0.614092</td>\n",
       "      <td>0.681545</td>\n",
       "      <td>0.434445</td>\n",
       "      <td>0.694098</td>\n",
       "      <td>0.636122</td>\n",
       "      <td>0.790837</td>\n",
       "      <td>0.390068</td>\n",
       "      <td>0.797844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.528676</td>\n",
       "      <td>0.447446</td>\n",
       "      <td>0.489238</td>\n",
       "      <td>0.435287</td>\n",
       "      <td>0.590058</td>\n",
       "      <td>0.387945</td>\n",
       "      <td>0.584459</td>\n",
       "      <td>0.375472</td>\n",
       "      <td>0.644025</td>\n",
       "      <td>...</td>\n",
       "      <td>0.510213</td>\n",
       "      <td>0.596567</td>\n",
       "      <td>0.597176</td>\n",
       "      <td>0.713618</td>\n",
       "      <td>0.481670</td>\n",
       "      <td>0.722227</td>\n",
       "      <td>0.599873</td>\n",
       "      <td>0.821583</td>\n",
       "      <td>0.398332</td>\n",
       "      <td>0.831940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>Hit</td>\n",
       "      <td>0.510992</td>\n",
       "      <td>0.429394</td>\n",
       "      <td>0.365172</td>\n",
       "      <td>0.435769</td>\n",
       "      <td>0.608950</td>\n",
       "      <td>0.371883</td>\n",
       "      <td>0.470900</td>\n",
       "      <td>0.396954</td>\n",
       "      <td>0.702145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.377530</td>\n",
       "      <td>0.605634</td>\n",
       "      <td>0.560029</td>\n",
       "      <td>0.735572</td>\n",
       "      <td>0.338763</td>\n",
       "      <td>0.738699</td>\n",
       "      <td>0.638658</td>\n",
       "      <td>0.851107</td>\n",
       "      <td>0.283776</td>\n",
       "      <td>0.852827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    target      p11x      p11y      p12x      p12y      p13x      p13y  \\\n",
       "361    Hit  0.503050  0.419582  0.416040  0.414611  0.581769  0.377122   \n",
       "62     Hit  0.533189  0.442137  0.452344  0.445124  0.639032  0.400441   \n",
       "320    Hit  0.516810  0.459327  0.505952  0.444943  0.540951  0.515194   \n",
       "92     Hit  0.515192  0.458072  0.444420  0.440142  0.579330  0.398837   \n",
       "146   Miss  0.529120  0.417938  0.443346  0.415669  0.440571  0.371223   \n",
       "292    Hit  0.525569  0.404123  0.495007  0.397034  0.593262  0.416326   \n",
       "238    Hit  0.483170  0.457052  0.430825  0.445130  0.501993  0.532554   \n",
       "296    Hit  0.519636  0.404974  0.489878  0.399595  0.601693  0.360763   \n",
       "29     Hit  0.484219  0.403663  0.383849  0.408965  0.553994  0.362381   \n",
       "172   Miss  0.402432  0.436768  0.269835  0.455657  0.493315  0.367383   \n",
       "299    Hit  0.520583  0.405997  0.446742  0.400851  0.606235  0.359410   \n",
       "376    Hit  0.495142  0.436183  0.418405  0.427938  0.548523  0.370330   \n",
       "191    Hit  0.621405  0.454633  0.494383  0.452022  0.711118  0.410144   \n",
       "80    Miss  0.618353  0.445937  0.440438  0.441000  0.766636  0.472223   \n",
       "354    Hit  0.485779  0.412876  0.451573  0.413236  0.496228  0.449266   \n",
       "95     Hit  0.521759  0.465703  0.443643  0.440022  0.562018  0.517918   \n",
       "289    Hit  0.528646  0.399928  0.487034  0.394961  0.588861  0.404374   \n",
       "5      Hit  0.527413  0.426842  0.468413  0.419309  0.592715  0.378062   \n",
       "389    Hit  0.528676  0.447446  0.489238  0.435287  0.590058  0.387945   \n",
       "369    Hit  0.510992  0.429394  0.365172  0.435769  0.608950  0.371883   \n",
       "\n",
       "         p14x      p14y      p15x  ...      p24x      p24y      p25x  \\\n",
       "361  0.565155  0.366704  0.658263  ...  0.412153  0.596647  0.543139   \n",
       "62   0.584126  0.406648  0.642906  ...  0.453835  0.597352  0.594969   \n",
       "320  0.588879  0.383308  0.580958  ...  0.479574  0.597164  0.541575   \n",
       "92   0.556529  0.383716  0.629344  ...  0.487831  0.619705  0.583745   \n",
       "146  0.359429  0.354078  0.337499  ...  0.423327  0.581829  0.554277   \n",
       "292  0.614458  0.337485  0.615729  ...  0.436914  0.563337  0.515209   \n",
       "238  0.560279  0.408881  0.569739  ...  0.466654  0.621661  0.562688   \n",
       "296  0.599202  0.348494  0.667281  ...  0.430535  0.568880  0.517743   \n",
       "29   0.478277  0.369116  0.601451  ...  0.379969  0.581254  0.546081   \n",
       "172  0.129527  0.508854  0.576681  ...  0.321310  0.609169  0.565974   \n",
       "299  0.573203  0.354532  0.661629  ...  0.426603  0.573568  0.527259   \n",
       "376  0.531161  0.366521  0.620191  ...  0.441399  0.610268  0.544821   \n",
       "191  0.524798  0.392989  0.790904  ...  0.487186  0.602513  0.609002   \n",
       "80   0.339824  0.415115  0.682432  ...  0.468068  0.601234  0.615019   \n",
       "354  0.584102  0.360402  0.522457  ...  0.406414  0.585930  0.530706   \n",
       "95   0.558337  0.386123  0.600624  ...  0.487938  0.623199  0.584935   \n",
       "289  0.622716  0.340616  0.650569  ...  0.422532  0.572516  0.518922   \n",
       "5    0.583697  0.370346  0.664180  ...  0.484469  0.576140  0.614092   \n",
       "389  0.584459  0.375472  0.644025  ...  0.510213  0.596567  0.597176   \n",
       "369  0.470900  0.396954  0.702145  ...  0.377530  0.605634  0.560029   \n",
       "\n",
       "         p25y      p26x      p26y      p27x      p27y      p28x      p28y  \n",
       "361  0.718073  0.350387  0.737964  0.573987  0.830337  0.275130  0.841092  \n",
       "62   0.706535  0.414527  0.718787  0.597082  0.812932  0.369997  0.823068  \n",
       "320  0.733155  0.412311  0.724134  0.550465  0.845990  0.297138  0.833496  \n",
       "92   0.757820  0.450874  0.757913  0.594270  0.879533  0.362966  0.877422  \n",
       "146  0.719643  0.301146  0.678487  0.598363  0.818809  0.327419  0.792805  \n",
       "292  0.699623  0.355171  0.701722  0.547543  0.810886  0.247410  0.815225  \n",
       "238  0.740640  0.432828  0.750742  0.549645  0.856688  0.316652  0.855822  \n",
       "296  0.697846  0.338478  0.698510  0.547251  0.808285  0.244911  0.812840  \n",
       "29   0.715429  0.348234  0.728652  0.565200  0.823092  0.283234  0.839835  \n",
       "172  0.709806  0.318391  0.742675  0.560715  0.830472  0.284802  0.836831  \n",
       "299  0.697328  0.345836  0.699722  0.548848  0.800270  0.256637  0.809263  \n",
       "376  0.736427  0.380078  0.740564  0.583659  0.852626  0.297847  0.856207  \n",
       "191  0.724780  0.439951  0.721126  0.591701  0.825236  0.370176  0.815689  \n",
       "80   0.741662  0.403197  0.729376  0.610431  0.836238  0.361827  0.829657  \n",
       "354  0.705357  0.346933  0.718999  0.563956  0.814286  0.287510  0.831913  \n",
       "95   0.757003  0.462113  0.758831  0.605657  0.880212  0.368238  0.876734  \n",
       "289  0.705704  0.397424  0.714909  0.543822  0.813635  0.257528  0.812281  \n",
       "5    0.681545  0.434445  0.694098  0.636122  0.790837  0.390068  0.797844  \n",
       "389  0.713618  0.481670  0.722227  0.599873  0.821583  0.398332  0.831940  \n",
       "369  0.735572  0.338763  0.738699  0.638658  0.851107  0.283776  0.852827  \n",
       "\n",
       "[20 rows x 25 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(frac=0.05, replace=False, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "796ff670",
   "metadata": {},
   "source": [
    "### Review the mean values for each of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "668f12d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p11x</th>\n",
       "      <th>p11y</th>\n",
       "      <th>p12x</th>\n",
       "      <th>p12y</th>\n",
       "      <th>p13x</th>\n",
       "      <th>p13y</th>\n",
       "      <th>p14x</th>\n",
       "      <th>p14y</th>\n",
       "      <th>p15x</th>\n",
       "      <th>p15y</th>\n",
       "      <th>...</th>\n",
       "      <th>p24x</th>\n",
       "      <th>p24y</th>\n",
       "      <th>p25x</th>\n",
       "      <th>p25y</th>\n",
       "      <th>p26x</th>\n",
       "      <th>p26y</th>\n",
       "      <th>p27x</th>\n",
       "      <th>p27y</th>\n",
       "      <th>p28x</th>\n",
       "      <th>p28y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hit</th>\n",
       "      <td>0.521063</td>\n",
       "      <td>0.434996</td>\n",
       "      <td>0.452048</td>\n",
       "      <td>0.428216</td>\n",
       "      <td>0.586606</td>\n",
       "      <td>0.403144</td>\n",
       "      <td>0.559816</td>\n",
       "      <td>0.380591</td>\n",
       "      <td>0.644546</td>\n",
       "      <td>0.361803</td>\n",
       "      <td>...</td>\n",
       "      <td>0.451901</td>\n",
       "      <td>0.593637</td>\n",
       "      <td>0.569916</td>\n",
       "      <td>0.713225</td>\n",
       "      <td>0.399128</td>\n",
       "      <td>0.720867</td>\n",
       "      <td>0.579428</td>\n",
       "      <td>0.822896</td>\n",
       "      <td>0.318687</td>\n",
       "      <td>0.826388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>0.545620</td>\n",
       "      <td>0.436627</td>\n",
       "      <td>0.414545</td>\n",
       "      <td>0.436863</td>\n",
       "      <td>0.576220</td>\n",
       "      <td>0.439927</td>\n",
       "      <td>0.371165</td>\n",
       "      <td>0.434391</td>\n",
       "      <td>0.568603</td>\n",
       "      <td>0.417865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.424980</td>\n",
       "      <td>0.590375</td>\n",
       "      <td>0.557316</td>\n",
       "      <td>0.713153</td>\n",
       "      <td>0.393533</td>\n",
       "      <td>0.713484</td>\n",
       "      <td>0.577753</td>\n",
       "      <td>0.809074</td>\n",
       "      <td>0.373149</td>\n",
       "      <td>0.814110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            p11x      p11y      p12x      p12y      p13x      p13y      p14x  \\\n",
       "target                                                                         \n",
       "Hit     0.521063  0.434996  0.452048  0.428216  0.586606  0.403144  0.559816   \n",
       "Miss    0.545620  0.436627  0.414545  0.436863  0.576220  0.439927  0.371165   \n",
       "\n",
       "            p14y      p15x      p15y  ...      p24x      p24y      p25x  \\\n",
       "target                                ...                                 \n",
       "Hit     0.380591  0.644546  0.361803  ...  0.451901  0.593637  0.569916   \n",
       "Miss    0.434391  0.568603  0.417865  ...  0.424980  0.590375  0.557316   \n",
       "\n",
       "            p25y      p26x      p26y      p27x      p27y      p28x      p28y  \n",
       "target                                                                        \n",
       "Hit     0.713225  0.399128  0.720867  0.579428  0.822896  0.318687  0.826388  \n",
       "Miss    0.713153  0.393533  0.713484  0.577753  0.809074  0.373149  0.814110  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('target').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "562cdd78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p11x</th>\n",
       "      <th>p11y</th>\n",
       "      <th>p12x</th>\n",
       "      <th>p12y</th>\n",
       "      <th>p13x</th>\n",
       "      <th>p13y</th>\n",
       "      <th>p14x</th>\n",
       "      <th>p14y</th>\n",
       "      <th>p15x</th>\n",
       "      <th>p15y</th>\n",
       "      <th>...</th>\n",
       "      <th>p24x</th>\n",
       "      <th>p24y</th>\n",
       "      <th>p25x</th>\n",
       "      <th>p25y</th>\n",
       "      <th>p26x</th>\n",
       "      <th>p26y</th>\n",
       "      <th>p27x</th>\n",
       "      <th>p27y</th>\n",
       "      <th>p28x</th>\n",
       "      <th>p28y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hit</th>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>...</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        p11x  p11y  p12x  p12y  p13x  p13y  p14x  p14y  p15x  p15y  ...  p24x  \\\n",
       "target                                                              ...         \n",
       "Hit      260   260   260   260   260   260   260   260   260   260  ...   260   \n",
       "Miss     145   145   145   145   145   145   145   145   145   145  ...   145   \n",
       "\n",
       "        p24y  p25x  p25y  p26x  p26y  p27x  p27y  p28x  p28y  \n",
       "target                                                        \n",
       "Hit      260   260   260   260   260   260   260   260   260  \n",
       "Miss     145   145   145   145   145   145   145   145   145  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('target').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec73b88c",
   "metadata": {},
   "source": [
    "**Average values for the FIRST set of landmark**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98559f1",
   "metadata": {},
   "source": [
    "Class: Successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aaba847e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.query(\"target == 'Hit'\").mean(numeric_only=True).plot(kind='bar', figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4afca7",
   "metadata": {},
   "source": [
    "Class: Failed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4c7b10fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.query(\"target == 'Miss'\").mean(numeric_only=True).plot(kind='bar', figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3985d3f1",
   "metadata": {},
   "source": [
    "Average values for each pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a2847b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='target'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('target').mean(numeric_only=True).plot(kind='bar', figsize=(16,12))"
   ]
  },
  {
   "attachments": {
    "frame_38.jpg": {
     "image/jpeg": "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"
    }
   },
   "cell_type": "markdown",
   "id": "91e6c986",
   "metadata": {},
   "source": [
    "The above information comes from the following pose. From the graph above, it can be seen that there are more observable differences in the positional values for the upper limb keypoints. This corresponds to the focus of the choreography of the song being arm movements.\n",
    " \n",
    "![frame_38.jpg](attachment:frame_38.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f948750",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 3 Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1301d09b",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`02_pose_model_training.py` runs through a series of Sckit-Learn models with GridSearchCV and saves the best model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4bbf5e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jinji\\Desktop\\GA_J\\DSI-SG-37 - Copy\\Capstone Project\\dance_c3\n",
      "file_name training1.csv model_name model1\n",
      "file_name training2.csv model_name model2\n",
      "file_name training3.csv model_name model3\n",
      "file_name training4.csv model_name model4\n",
      "file_name training5.csv model_name model5\n",
      "file_name training6.csv model_name model6\n",
      "file_name training7.csv model_name model7\n",
      "file_name training8.csv model_name model8\n",
      "file_name training9.csv model_name model9\n",
      "file_name training10.csv model_name model10\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df = pd.read_excel('data/key_poses.xlsx')\n",
    "print(os.getcwd())\n",
    "# Drop duplicates for file_name and model_name columns\n",
    "df_unique = df[['file_name', 'model_name']].drop_duplicates()\n",
    "\n",
    "# Iterate over the unique combinations of file_name and model_name\n",
    "for index, row in df_unique.iterrows():\n",
    "\n",
    "    file_name = row['file_name']\n",
    "    model_name = row['model_name']\n",
    "    print('file_name', file_name, 'model_name', model_name)\n",
    "    # Create arguments for the script\n",
    "    args = ['--training-data', str(file_name), \n",
    "            \"--model-name\", str(model_name)]\n",
    "    \n",
    "\n",
    "    run_script(\"02_pose_model_training.py\", args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52556ae8",
   "metadata": {},
   "source": [
    "**Summarising the evaluation metrics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f04f1842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " File_list: ['model\\\\model10_metadata.txt', 'model\\\\model1_metadata.txt', 'model\\\\model2_metadata.txt', 'model\\\\model3_metadata.txt', 'model\\\\model4_metadata.txt', 'model\\\\model5_metadata.txt', 'model\\\\model6_metadata.txt', 'model\\\\model7_metadata.txt', 'model\\\\model8_metadata.txt', 'model\\\\model9_metadata.txt']\n",
      "File: model\\model10_metadata.txt\n",
      "\n",
      " dict_data:\n",
      "{'Model Name': 'KNN GridSearch', 'Training Score (CV)': 0.8333333333333334, 'Accuracy': 0.75, 'Precision': 0.75, 'Recall': 0.75, 'F1 Score': 0.75, 'Log Loss': 9.010913347279288, 'ROC AUC': 0.75}\n",
      "\n",
      " File_list: ['model\\\\model10_metadata.txt', 'model\\\\model1_metadata.txt', 'model\\\\model2_metadata.txt', 'model\\\\model3_metadata.txt', 'model\\\\model4_metadata.txt', 'model\\\\model5_metadata.txt', 'model\\\\model6_metadata.txt', 'model\\\\model7_metadata.txt', 'model\\\\model8_metadata.txt', 'model\\\\model9_metadata.txt']\n",
      "File: model\\model1_metadata.txt\n",
      "\n",
      " dict_data:\n",
      "{'Model Name': 'KNN GridSearch', 'Training Score (CV)': 0.8162393162393163, 'Accuracy': 0.8269230769230769, 'Precision': 0.8269230769230769, 'Recall': 0.8269230769230769, 'F1 Score': 0.8269230769230769, 'Log Loss': 1.032076734578871, 'ROC AUC': 0.8505639097744362}\n",
      "\n",
      " File_list: ['model\\\\model10_metadata.txt', 'model\\\\model1_metadata.txt', 'model\\\\model2_metadata.txt', 'model\\\\model3_metadata.txt', 'model\\\\model4_metadata.txt', 'model\\\\model5_metadata.txt', 'model\\\\model6_metadata.txt', 'model\\\\model7_metadata.txt', 'model\\\\model8_metadata.txt', 'model\\\\model9_metadata.txt']\n",
      "File: model\\model2_metadata.txt\n",
      "\n",
      " dict_data:\n",
      "{'Model Name': 'GradientBoostingClassifier', 'Training Score (CV)': 0.8260869565217391, 'Accuracy': 0.8205128205128205, 'Precision': 0.8205128205128205, 'Recall': 0.8205128205128205, 'F1 Score': 0.8205128205128205, 'Log Loss': 0.39140963389457195, 'ROC AUC': 0.9320987654320988}\n",
      "\n",
      " File_list: ['model\\\\model10_metadata.txt', 'model\\\\model1_metadata.txt', 'model\\\\model2_metadata.txt', 'model\\\\model3_metadata.txt', 'model\\\\model4_metadata.txt', 'model\\\\model5_metadata.txt', 'model\\\\model6_metadata.txt', 'model\\\\model7_metadata.txt', 'model\\\\model8_metadata.txt', 'model\\\\model9_metadata.txt']\n",
      "File: model\\model3_metadata.txt\n",
      "\n",
      " dict_data:\n",
      "{'Model Name': 'GradientBoostingClassifier', 'Training Score (CV)': 0.8534621578099838, 'Accuracy': 0.8125, 'Precision': 0.8125, 'Recall': 0.8125, 'F1 Score': 0.8125, 'Log Loss': 0.5596725916005462, 'ROC AUC': 0.8727272727272728}\n",
      "\n",
      " File_list: ['model\\\\model10_metadata.txt', 'model\\\\model1_metadata.txt', 'model\\\\model2_metadata.txt', 'model\\\\model3_metadata.txt', 'model\\\\model4_metadata.txt', 'model\\\\model5_metadata.txt', 'model\\\\model6_metadata.txt', 'model\\\\model7_metadata.txt', 'model\\\\model8_metadata.txt', 'model\\\\model9_metadata.txt']\n",
      "File: model\\model4_metadata.txt\n",
      "\n",
      " dict_data:\n",
      "{'Model Name': 'LogisticRegression', 'Training Score (CV)': 0.8101112723754232, 'Accuracy': 0.8888888888888888, 'Precision': 0.8888888888888888, 'Recall': 0.8888888888888888, 'F1 Score': 0.8888888888888888, 'Log Loss': 0.37577439931327405, 'ROC AUC': 0.8923076923076922}\n",
      "\n",
      " File_list: ['model\\\\model10_metadata.txt', 'model\\\\model1_metadata.txt', 'model\\\\model2_metadata.txt', 'model\\\\model3_metadata.txt', 'model\\\\model4_metadata.txt', 'model\\\\model5_metadata.txt', 'model\\\\model6_metadata.txt', 'model\\\\model7_metadata.txt', 'model\\\\model8_metadata.txt', 'model\\\\model9_metadata.txt']\n",
      "File: model\\model5_metadata.txt\n",
      "\n",
      " dict_data:\n",
      "{'Model Name': 'DecisionTree', 'Training Score (CV)': 0.8034552845528454, 'Accuracy': 0.7142857142857143, 'Precision': 0.7142857142857143, 'Recall': 0.7142857142857143, 'F1 Score': 0.7142857142857143, 'Log Loss': 5.438054886900242, 'ROC AUC': 0.6499999999999999}\n",
      "\n",
      " File_list: ['model\\\\model10_metadata.txt', 'model\\\\model1_metadata.txt', 'model\\\\model2_metadata.txt', 'model\\\\model3_metadata.txt', 'model\\\\model4_metadata.txt', 'model\\\\model5_metadata.txt', 'model\\\\model6_metadata.txt', 'model\\\\model7_metadata.txt', 'model\\\\model8_metadata.txt', 'model\\\\model9_metadata.txt']\n",
      "File: model\\model6_metadata.txt\n",
      "\n",
      " dict_data:\n",
      "{'Model Name': 'KNN GridSearch', 'Training Score (CV)': 0.8499702911467617, 'Accuracy': 0.8333333333333334, 'Precision': 0.8333333333333334, 'Recall': 0.8333333333333334, 'F1 Score': 0.8333333333333334, 'Log Loss': 3.350211372706403, 'ROC AUC': 0.8428571428571429}\n",
      "\n",
      " File_list: ['model\\\\model10_metadata.txt', 'model\\\\model1_metadata.txt', 'model\\\\model2_metadata.txt', 'model\\\\model3_metadata.txt', 'model\\\\model4_metadata.txt', 'model\\\\model5_metadata.txt', 'model\\\\model6_metadata.txt', 'model\\\\model7_metadata.txt', 'model\\\\model8_metadata.txt', 'model\\\\model9_metadata.txt']\n",
      "File: model\\model7_metadata.txt\n",
      "\n",
      " dict_data:\n",
      "{'Model Name': 'KNN GridSearch', 'Training Score (CV)': 0.8711656441717791, 'Accuracy': 0.8363636363636363, 'Precision': 0.8363636363636363, 'Recall': 0.8363636363636363, 'F1 Score': 0.8363636363636363, 'Log Loss': 5.898052372764625, 'ROC AUC': 0.7964285714285715}\n",
      "\n",
      " File_list: ['model\\\\model10_metadata.txt', 'model\\\\model1_metadata.txt', 'model\\\\model2_metadata.txt', 'model\\\\model3_metadata.txt', 'model\\\\model4_metadata.txt', 'model\\\\model5_metadata.txt', 'model\\\\model6_metadata.txt', 'model\\\\model7_metadata.txt', 'model\\\\model8_metadata.txt', 'model\\\\model9_metadata.txt']\n",
      "File: model\\model8_metadata.txt\n",
      "\n",
      " dict_data:\n",
      "{'Model Name': 'GradientBoostingClassifier', 'Training Score (CV)': 0.9063655913978494, 'Accuracy': 0.8571428571428571, 'Precision': 0.8571428571428571, 'Recall': 0.8571428571428571, 'F1 Score': 0.8571428571428571, 'Log Loss': 0.5166530151109608, 'ROC AUC': 0.8777777777777775}\n",
      "\n",
      " File_list: ['model\\\\model10_metadata.txt', 'model\\\\model1_metadata.txt', 'model\\\\model2_metadata.txt', 'model\\\\model3_metadata.txt', 'model\\\\model4_metadata.txt', 'model\\\\model5_metadata.txt', 'model\\\\model6_metadata.txt', 'model\\\\model7_metadata.txt', 'model\\\\model8_metadata.txt', 'model\\\\model9_metadata.txt']\n",
      "File: model\\model9_metadata.txt\n",
      "\n",
      " dict_data:\n",
      "{'Model Name': 'GradientBoostingClassifier', 'Training Score (CV)': 0.8438818565400844, 'Accuracy': 0.7037037037037037, 'Precision': 0.7037037037037037, 'Recall': 0.7037037037037037, 'F1 Score': 0.7037037037037037, 'Log Loss': 0.6800878740869827, 'ROC AUC': 0.8058823529411765}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Training Score (CV)</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Log Loss</th>\n",
       "      <th>ROC AUC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean ± std</th>\n",
       "      <th>mean ± std</th>\n",
       "      <th>mean ± std</th>\n",
       "      <th>mean ± std</th>\n",
       "      <th>mean ± std</th>\n",
       "      <th>mean ± std</th>\n",
       "      <th>mean ± std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DecisionTree</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.71</td>\n",
       "      <td>5.44</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoostingClassifier</th>\n",
       "      <td>0.86 ± 0.03</td>\n",
       "      <td>0.80 ± 0.07</td>\n",
       "      <td>0.80 ± 0.07</td>\n",
       "      <td>0.80 ± 0.07</td>\n",
       "      <td>0.80 ± 0.07</td>\n",
       "      <td>0.54 ± 0.12</td>\n",
       "      <td>0.87 ± 0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN GridSearch</th>\n",
       "      <td>0.84 ± 0.02</td>\n",
       "      <td>0.81 ± 0.04</td>\n",
       "      <td>0.81 ± 0.04</td>\n",
       "      <td>0.81 ± 0.04</td>\n",
       "      <td>0.81 ± 0.04</td>\n",
       "      <td>4.82 ± 3.43</td>\n",
       "      <td>0.81 ± 0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Training Score (CV)     Accuracy    Precision  \\\n",
       "                                    mean ± std   mean ± std   mean ± std   \n",
       "Model Name                                                                 \n",
       "DecisionTree                              0.80         0.71         0.71   \n",
       "GradientBoostingClassifier         0.86 ± 0.03  0.80 ± 0.07  0.80 ± 0.07   \n",
       "KNN GridSearch                     0.84 ± 0.02  0.81 ± 0.04  0.81 ± 0.04   \n",
       "LogisticRegression                        0.81         0.89         0.89   \n",
       "\n",
       "                                 Recall     F1 Score     Log Loss      ROC AUC  \n",
       "                             mean ± std   mean ± std   mean ± std   mean ± std  \n",
       "Model Name                                                                      \n",
       "DecisionTree                       0.71         0.71         5.44         0.65  \n",
       "GradientBoostingClassifier  0.80 ± 0.07  0.80 ± 0.07  0.54 ± 0.12  0.87 ± 0.05  \n",
       "KNN GridSearch              0.81 ± 0.04  0.81 ± 0.04  4.82 ± 3.43  0.81 ± 0.05  \n",
       "LogisticRegression                 0.89         0.89         0.38         0.89  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import io\n",
    "import ast\n",
    "\n",
    "\n",
    "subfolder = 'model'\n",
    "file_pattern = 'model*_metadata.txt'\n",
    "\n",
    "# Generate a list of all relevant text files\n",
    "file_list = glob.glob(os.path.join(subfolder, file_pattern))\n",
    "\n",
    "df_all = pd.DataFrame()\n",
    "\n",
    "\n",
    "\n",
    "for file in file_list:\n",
    "    print('\\n File_list:', file_list)\n",
    "    print('File:', file)\n",
    "\n",
    "    with open(file, 'r') as f:\n",
    "        content = f.read()\n",
    "\n",
    "    # Find the start and end indices of the scoring metrics\n",
    "    start_index = content.find(\"{'Model Name'\")\n",
    "    end_index = content.find(\"}\\n\", start_index) + 1  # include the closing brace\n",
    "\n",
    "    # Extract the scoring metrics and convert them to a DataFrame\n",
    "    dict_str = content[start_index:end_index]\n",
    "    dict_data = ast.literal_eval(dict_str)\n",
    "    df = pd.DataFrame([dict_data])\n",
    "    print('\\n dict_data:')\n",
    "    print(dict_data)\n",
    "\n",
    "    # Append the DataFrame to the main DataFrame\n",
    "    df_all = pd.concat([df_all, df])\n",
    "\n",
    "# Calculate the mean and standard deviation for each model and metric\n",
    "df_agg = df_all.groupby('Model Name').agg(['mean', 'std'])\n",
    "\n",
    "# Format the final DataFrame\n",
    "df_final = df_agg.apply(lambda row: ['{:.2f} ± {:.2f}'.format(mean, std) if not pd.isna(std) else '{:.2f}'.format(mean) for mean, std in zip(row[::2], row[1::2])], axis=1)\n",
    "\n",
    "# Convert the final Series to DataFrame\n",
    "df_final = df_final.apply(pd.Series)\n",
    "\n",
    "# Create a MultiIndex for the columns to match df_agg\n",
    "df_final.columns = pd.MultiIndex.from_product([df_agg.columns.get_level_values(0).unique(), ['mean ± std']])\n",
    "\n",
    "df_final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8851a70b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Training Score (CV)</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Log Loss</th>\n",
       "      <th>ROC AUC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DecisionTree</th>\n",
       "      <td>0.8034552845528454 ± 0.0</td>\n",
       "      <td>0.7142857142857143 ± 0.0</td>\n",
       "      <td>0.7142857142857143 ± 0.0</td>\n",
       "      <td>0.7142857142857143 ± 0.0</td>\n",
       "      <td>0.7142857142857143 ± 0.0</td>\n",
       "      <td>5.438054886900242 ± 0.0</td>\n",
       "      <td>0.6499999999999999 ± 0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoostingClassifier</th>\n",
       "      <td>0.8574491405674142 ± 0.034527153456120445</td>\n",
       "      <td>0.7984648453398453 ± 0.06609559190773584</td>\n",
       "      <td>0.7984648453398453 ± 0.06609559190773584</td>\n",
       "      <td>0.7984648453398453 ± 0.06609559190773584</td>\n",
       "      <td>0.7984648453398453 ± 0.06609559190773584</td>\n",
       "      <td>0.5369557786732654 ± 0.11916198050734943</td>\n",
       "      <td>0.8721215422195814 ± 0.051695445997631886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN GridSearch</th>\n",
       "      <td>0.8426771462227977 ± 0.023459540858108915</td>\n",
       "      <td>0.8116550116550116 ± 0.0412913229181385</td>\n",
       "      <td>0.8116550116550116 ± 0.0412913229181385</td>\n",
       "      <td>0.8116550116550116 ± 0.0412913229181385</td>\n",
       "      <td>0.8116550116550116 ± 0.0412913229181385</td>\n",
       "      <td>4.8228134568322965 ± 3.4270765585413074</td>\n",
       "      <td>0.8099624060150377 ± 0.04658039871434428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.8101112723754232 ± 0.0</td>\n",
       "      <td>0.8888888888888888 ± 0.0</td>\n",
       "      <td>0.8888888888888888 ± 0.0</td>\n",
       "      <td>0.8888888888888888 ± 0.0</td>\n",
       "      <td>0.8888888888888888 ± 0.0</td>\n",
       "      <td>0.37577439931327405 ± 0.0</td>\n",
       "      <td>0.8923076923076922 ± 0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Training Score (CV)  \\\n",
       "Model Name                                                              \n",
       "DecisionTree                                 0.8034552845528454 ± 0.0   \n",
       "GradientBoostingClassifier  0.8574491405674142 ± 0.034527153456120445   \n",
       "KNN GridSearch              0.8426771462227977 ± 0.023459540858108915   \n",
       "LogisticRegression                           0.8101112723754232 ± 0.0   \n",
       "\n",
       "                                                            Accuracy  \\\n",
       "Model Name                                                             \n",
       "DecisionTree                                0.7142857142857143 ± 0.0   \n",
       "GradientBoostingClassifier  0.7984648453398453 ± 0.06609559190773584   \n",
       "KNN GridSearch               0.8116550116550116 ± 0.0412913229181385   \n",
       "LogisticRegression                          0.8888888888888888 ± 0.0   \n",
       "\n",
       "                                                           Precision  \\\n",
       "Model Name                                                             \n",
       "DecisionTree                                0.7142857142857143 ± 0.0   \n",
       "GradientBoostingClassifier  0.7984648453398453 ± 0.06609559190773584   \n",
       "KNN GridSearch               0.8116550116550116 ± 0.0412913229181385   \n",
       "LogisticRegression                          0.8888888888888888 ± 0.0   \n",
       "\n",
       "                                                              Recall  \\\n",
       "Model Name                                                             \n",
       "DecisionTree                                0.7142857142857143 ± 0.0   \n",
       "GradientBoostingClassifier  0.7984648453398453 ± 0.06609559190773584   \n",
       "KNN GridSearch               0.8116550116550116 ± 0.0412913229181385   \n",
       "LogisticRegression                          0.8888888888888888 ± 0.0   \n",
       "\n",
       "                                                            F1 Score  \\\n",
       "Model Name                                                             \n",
       "DecisionTree                                0.7142857142857143 ± 0.0   \n",
       "GradientBoostingClassifier  0.7984648453398453 ± 0.06609559190773584   \n",
       "KNN GridSearch               0.8116550116550116 ± 0.0412913229181385   \n",
       "LogisticRegression                          0.8888888888888888 ± 0.0   \n",
       "\n",
       "                                                            Log Loss  \\\n",
       "Model Name                                                             \n",
       "DecisionTree                                 5.438054886900242 ± 0.0   \n",
       "GradientBoostingClassifier  0.5369557786732654 ± 0.11916198050734943   \n",
       "KNN GridSearch               4.8228134568322965 ± 3.4270765585413074   \n",
       "LogisticRegression                         0.37577439931327405 ± 0.0   \n",
       "\n",
       "                                                              ROC AUC  \n",
       "Model Name                                                             \n",
       "DecisionTree                                 0.6499999999999999 ± 0.0  \n",
       "GradientBoostingClassifier  0.8721215422195814 ± 0.051695445997631886  \n",
       "KNN GridSearch               0.8099624060150377 ± 0.04658039871434428  \n",
       "LogisticRegression                           0.8923076923076922 ± 0.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import io\n",
    "import ast\n",
    "\n",
    "# Define the subfolder and the naming pattern for the text files\n",
    "subfolder = 'model'\n",
    "file_pattern = 'model*_metadata.txt'\n",
    "\n",
    "# Generate a list of all relevant text files\n",
    "file_list = glob.glob(os.path.join(subfolder, file_pattern))\n",
    "\n",
    "# Initialize an empty DataFrame to store the data from all files\n",
    "df_all = pd.DataFrame()\n",
    "\n",
    "# Loop over all files\n",
    "for file in file_list:\n",
    "    # Open the file and read its content\n",
    "    with open(file, 'r') as f:\n",
    "        content = f.read()\n",
    "\n",
    "    # Find the start and end indices of the scoring metrics\n",
    "    start_index = content.find(\"{'Model Name'\")\n",
    "    end_index = content.find(\"}\\n\", start_index) + 1  # include the closing brace\n",
    "\n",
    "    # Extract the scoring metrics and convert them to a DataFrame\n",
    "    dict_str = content[start_index:end_index]\n",
    "    dict_data = ast.literal_eval(dict_str)\n",
    "    df = pd.DataFrame([dict_data])\n",
    "\n",
    "    # Append the DataFrame to the main DataFrame\n",
    "    df_all = pd.concat([df_all, df])\n",
    "\n",
    "# Calculate the mean and standard deviation for each model and metric\n",
    "df_mean = df_all.groupby('Model Name').mean()\n",
    "df_std = df_all.groupby('Model Name').std()\n",
    "\n",
    "# Replace NaN values in df_std with 0\n",
    "df_std.fillna(0, inplace=True)\n",
    "\n",
    "# Create a DataFrame that combines the mean and standard deviation\n",
    "df_final = df_mean.astype(str) + ' ± ' + df_std.astype(str)\n",
    "\n",
    "df_final\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545a3c83",
   "metadata": {},
   "source": [
    "**Model Prediction**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8f7b9c",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "``03_pose_predictions.py`` processes new input video and based on the pre-assigned timeframe and models, it access the user's pose based on timestamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d13ac513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.004 0.996]\n",
      "dance [0.    0.    0.    0.002 0.998]\n",
      "dance [0.    0.    0.    0.002 0.998]\n",
      "dance [0.    0.    0.    0.007 0.993]\n",
      "dance [0.    0.    0.    0.009 0.991]\n",
      "dance [0.    0.    0.    0.014 0.986]\n",
      "dance [0.    0.    0.    0.016 0.984]\n",
      "dance [0.    0.    0.    0.018 0.982]\n",
      "dance [0.    0.    0.    0.004 0.996]\n",
      "dance [0.    0.    0.    0.005 0.995]\n",
      "dance [0.    0.    0.    0.014 0.986]\n",
      "dance [0.    0.    0.    0.052 0.948]\n",
      "dance [0.    0.    0.    0.281 0.719]\n",
      "Y [0.    0.    0.    0.507 0.493]\n",
      "Y [0.    0.    0.    0.687 0.313]\n",
      "Y [0.    0.    0.    0.658 0.342]\n",
      "dance [0.    0.    0.    0.457 0.543]\n",
      "dance [0.    0.    0.    0.409 0.591]\n",
      "Y [0.   0.   0.   0.57 0.43]\n",
      "Y [0.    0.    0.    0.741 0.259]\n",
      "Y [0.    0.    0.    0.961 0.039]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.998 0.002]\n",
      "Y [0.    0.    0.    0.996 0.004]\n",
      "Y [0.    0.    0.    0.993 0.007]\n",
      "Y [0.    0.    0.    0.996 0.004]\n",
      "Y [0.    0.    0.    0.993 0.007]\n",
      "Y [0.   0.   0.   0.91 0.09]\n",
      "Y [0.    0.    0.    0.948 0.052]\n",
      "Y [0.   0.   0.   0.99 0.01]\n",
      "Y [0.    0.    0.    0.995 0.005]\n",
      "Y [0.    0.    0.    0.994 0.006]\n",
      "Y [0.   0.   0.   0.99 0.01]\n",
      "Y [0.   0.   0.   0.99 0.01]\n",
      "Y [0.    0.    0.    0.947 0.053]\n",
      "Y [0.    0.    0.    0.914 0.086]\n",
      "Y [0.    0.    0.    0.929 0.071]\n",
      "Y [0.   0.   0.   0.94 0.06]\n",
      "Y [0.    0.    0.    0.985 0.015]\n",
      "Y [0.    0.    0.    0.994 0.006]\n",
      "Y [0.    0.    0.    0.994 0.006]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.998 0.002]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.    0.973 0.027]\n",
      "Y [0.    0.    0.    0.605 0.395]\n",
      "dance [0.   0.   0.   0.07 0.93]\n",
      "dance [0.    0.    0.    0.159 0.841]\n",
      "Y [0.    0.    0.001 0.919 0.081]\n",
      "M [0.    0.    0.692 0.213 0.095]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0.001 0.    0.999 0.    0.   ]\n",
      "M [0.003 0.    0.997 0.    0.   ]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0.001 0.    0.999 0.    0.   ]\n",
      "M [0.001 0.    0.999 0.    0.   ]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0.    0.    0.999 0.    0.   ]\n",
      "M [0.001 0.    0.997 0.    0.002]\n",
      "M [0.001 0.    0.998 0.    0.001]\n",
      "M [0.002 0.    0.997 0.    0.002]\n",
      "M [0.001 0.    0.998 0.    0.001]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0.002 0.    0.998 0.    0.   ]\n",
      "M [0.001 0.    0.999 0.    0.001]\n",
      "M [0.001 0.    0.937 0.    0.062]\n",
      "dance [0.    0.    0.383 0.009 0.607]\n",
      "dance [0.    0.    0.064 0.202 0.734]\n",
      "dance [0.    0.    0.004 0.283 0.713]\n",
      "Y [0.    0.    0.002 0.585 0.413]\n",
      "dance [0.    0.    0.001 0.389 0.61 ]\n",
      "dance [0.    0.    0.    0.354 0.645]\n",
      "dance [0.    0.    0.    0.101 0.899]\n",
      "dance [0.    0.    0.    0.078 0.922]\n",
      "dance [0.    0.    0.    0.046 0.954]\n",
      "dance [0.    0.    0.    0.051 0.949]\n",
      "dance [0.    0.    0.    0.081 0.919]\n",
      "dance [0.    0.    0.001 0.046 0.953]\n",
      "dance [0.    0.    0.    0.028 0.972]\n",
      "dance [0.    0.    0.001 0.045 0.954]\n",
      "dance [0.    0.    0.001 0.095 0.904]\n",
      "dance [0.    0.    0.002 0.077 0.92 ]\n",
      "dance [0.    0.    0.012 0.21  0.777]\n",
      "dance [0.    0.    0.017 0.463 0.52 ]\n",
      "Y [0.    0.    0.004 0.858 0.138]\n",
      "Y [0.    0.    0.001 0.902 0.097]\n",
      "Y [0.    0.    0.    0.99  0.009]\n",
      "Y [0.    0.    0.    0.964 0.035]\n",
      "Y [0.    0.    0.    0.983 0.017]\n",
      "Y [0.    0.    0.    0.993 0.007]\n",
      "Y [0.    0.    0.    0.998 0.002]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.995 0.005]\n",
      "Y [0.    0.    0.    0.995 0.005]\n",
      "Y [0.    0.    0.    0.997 0.003]\n",
      "Y [0.    0.    0.    0.984 0.016]\n",
      "Y [0.    0.    0.    0.993 0.007]\n",
      "Y [0.    0.    0.    0.997 0.003]\n",
      "Y [0.    0.    0.    0.958 0.042]\n",
      "Y [0.    0.    0.    0.991 0.009]\n",
      "Y [0.    0.    0.    0.728 0.272]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0.    0.    0.    0.005 0.995]\n",
      "dance [0.    0.    0.    0.027 0.973]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0.    0.    0.001 0.    0.999]\n",
      "dance [0.    0.    0.124 0.    0.876]\n",
      "dance [0.    0.001 0.14  0.    0.858]\n",
      "dance [0.    0.011 0.276 0.    0.713]\n",
      "dance [0.    0.063 0.361 0.    0.575]\n",
      "M [0.    0.126 0.593 0.    0.282]\n",
      "M [0.    0.116 0.629 0.    0.255]\n",
      "dance [0.    0.069 0.292 0.    0.639]\n",
      "dance [0.    0.017 0.025 0.    0.958]\n",
      "dance [0.    0.002 0.006 0.    0.992]\n",
      "dance [0.    0.    0.002 0.    0.998]\n",
      "dance [0.    0.    0.001 0.    0.999]\n",
      "dance [0.    0.    0.003 0.    0.997]\n",
      "dance [0.    0.    0.    0.    0.999]\n",
      "dance [0.    0.    0.001 0.    0.999]\n",
      "dance [0.    0.    0.001 0.    0.999]\n",
      "dance [0.    0.    0.001 0.    0.999]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.    0.999]\n",
      "dance [0.    0.    0.001 0.001 0.999]\n",
      "dance [0.    0.    0.001 0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.    0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.002 0.998]\n",
      "dance [0.    0.    0.    0.003 0.997]\n",
      "dance [0.    0.    0.    0.007 0.993]\n",
      "dance [0.    0.    0.    0.006 0.994]\n",
      "dance [0.   0.   0.   0.01 0.99]\n",
      "dance [0.    0.    0.    0.019 0.981]\n",
      "dance [0.    0.    0.    0.023 0.976]\n",
      "dance [0.    0.    0.    0.145 0.854]\n",
      "dance [0.    0.    0.    0.492 0.507]\n",
      "Y [0.    0.    0.    0.926 0.074]\n",
      "Y [0.    0.    0.    0.966 0.034]\n",
      "Y [0.    0.    0.    0.959 0.041]\n",
      "Y [0.    0.    0.    0.905 0.094]\n",
      "dance [0.    0.    0.006 0.202 0.792]\n",
      "dance [0.    0.    0.005 0.008 0.987]\n",
      "dance [0.    0.    0.004 0.004 0.992]\n",
      "dance [0.    0.    0.001 0.017 0.982]\n",
      "dance [0.    0.    0.    0.018 0.982]\n",
      "dance [0.    0.    0.    0.013 0.986]\n",
      "dance [0.    0.    0.    0.016 0.984]\n",
      "dance [0.    0.    0.    0.016 0.984]\n",
      "dance [0.    0.    0.    0.018 0.982]\n",
      "dance [0.    0.    0.    0.032 0.968]\n",
      "dance [0.    0.    0.    0.097 0.903]\n",
      "dance [0.    0.    0.    0.472 0.528]\n",
      "Y [0.    0.    0.    0.607 0.393]\n",
      "Y [0.    0.    0.    0.513 0.487]\n",
      "dance [0.    0.    0.    0.447 0.553]\n",
      "dance [0.    0.    0.    0.398 0.602]\n",
      "dance [0.    0.    0.    0.373 0.627]\n",
      "dance [0.    0.    0.    0.349 0.651]\n",
      "dance [0.    0.    0.    0.345 0.655]\n",
      "dance [0.    0.    0.    0.296 0.704]\n",
      "dance [0.    0.    0.    0.313 0.687]\n",
      "dance [0.   0.   0.   0.33 0.67]\n",
      "dance [0.    0.    0.    0.386 0.614]\n",
      "dance [0.    0.    0.    0.418 0.582]\n",
      "dance [0.    0.    0.    0.403 0.597]\n",
      "dance [0.    0.    0.    0.391 0.609]\n",
      "dance [0.    0.    0.    0.384 0.616]\n",
      "dance [0.    0.    0.    0.367 0.633]\n",
      "dance [0.    0.    0.    0.326 0.674]\n",
      "dance [0.    0.    0.    0.114 0.886]\n",
      "dance [0.    0.    0.    0.038 0.962]\n",
      "dance [0.    0.    0.006 0.004 0.991]\n",
      "dance [0.    0.    0.018 0.001 0.981]\n",
      "dance [0.    0.    0.052 0.    0.948]\n",
      "dance [0.    0.    0.052 0.    0.948]\n",
      "dance [0.    0.    0.038 0.    0.962]\n",
      "dance [0.    0.    0.031 0.    0.968]\n",
      "dance [0.    0.    0.028 0.    0.972]\n",
      "dance [0.    0.    0.033 0.    0.967]\n",
      "dance [0.    0.    0.036 0.    0.963]\n",
      "dance [0.    0.    0.043 0.    0.957]\n",
      "dance [0.    0.    0.034 0.    0.965]\n",
      "dance [0.    0.    0.035 0.    0.964]\n",
      "dance [0.    0.    0.048 0.    0.952]\n",
      "dance [0.    0.    0.053 0.    0.947]\n",
      "dance [0.    0.    0.061 0.    0.939]\n",
      "dance [0.    0.    0.079 0.    0.921]\n",
      "dance [0.    0.    0.082 0.    0.918]\n",
      "dance [0.    0.    0.081 0.    0.918]\n",
      "dance [0.    0.    0.072 0.    0.927]\n",
      "dance [0.    0.    0.067 0.    0.933]\n",
      "dance [0.    0.    0.067 0.    0.933]\n",
      "dance [0.    0.    0.059 0.    0.941]\n",
      "dance [0.    0.    0.061 0.    0.939]\n",
      "dance [0.    0.    0.048 0.    0.952]\n",
      "dance [0.    0.    0.041 0.    0.959]\n",
      "dance [0.    0.    0.051 0.001 0.949]\n",
      "dance [0.    0.    0.01  0.006 0.984]\n",
      "dance [0.    0.    0.009 0.006 0.985]\n",
      "dance [0.    0.    0.01  0.003 0.987]\n",
      "dance [0.    0.    0.007 0.002 0.992]\n",
      "dance [0.    0.    0.006 0.001 0.992]\n",
      "dance [0.    0.    0.006 0.002 0.992]\n",
      "dance [0.    0.    0.006 0.002 0.991]\n",
      "dance [0.    0.    0.003 0.004 0.992]\n",
      "dance [0.    0.    0.002 0.004 0.994]\n",
      "dance [0.    0.    0.002 0.002 0.996]\n",
      "dance [0.    0.    0.002 0.002 0.996]\n",
      "dance [0.    0.    0.003 0.002 0.995]\n",
      "dance [0.    0.    0.003 0.005 0.992]\n",
      "dance [0.    0.    0.004 0.007 0.99 ]\n",
      "dance [0.    0.    0.004 0.007 0.989]\n",
      "dance [0.    0.    0.003 0.003 0.994]\n",
      "dance [0.    0.    0.003 0.003 0.994]\n",
      "dance [0.    0.    0.003 0.009 0.988]\n",
      "dance [0.    0.    0.003 0.011 0.986]\n",
      "dance [0.    0.    0.004 0.024 0.972]\n",
      "dance [0.    0.    0.005 0.129 0.866]\n",
      "dance [0.    0.    0.001 0.011 0.988]\n",
      "dance [0.    0.    0.001 0.004 0.995]\n",
      "dance [0.    0.    0.001 0.002 0.996]\n",
      "dance [0.    0.    0.002 0.001 0.998]\n",
      "dance [0.    0.    0.002 0.    0.997]\n",
      "dance [0.    0.    0.002 0.    0.998]\n",
      "dance [0.    0.    0.002 0.    0.998]\n",
      "dance [0.    0.    0.002 0.001 0.997]\n",
      "dance [0.    0.    0.002 0.001 0.997]\n",
      "dance [0.    0.    0.002 0.001 0.997]\n",
      "dance [0.    0.    0.001 0.    0.999]\n",
      "dance [0.    0.    0.001 0.    0.999]\n",
      "dance [0.    0.    0.001 0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.998]\n",
      "dance [0.    0.    0.    0.003 0.996]\n",
      "dance [0.    0.    0.    0.004 0.995]\n",
      "dance [0.    0.    0.    0.049 0.951]\n",
      "dance [0.    0.    0.    0.199 0.801]\n",
      "dance [0.    0.    0.    0.136 0.864]\n",
      "dance [0.    0.    0.    0.344 0.656]\n",
      "dance [0.    0.    0.    0.456 0.544]\n",
      "Y [0.    0.    0.    0.655 0.344]\n",
      "dance [0.    0.    0.    0.459 0.54 ]\n",
      "dance [0.  0.  0.  0.3 0.7]\n",
      "dance [0.    0.    0.    0.265 0.735]\n",
      "dance [0.    0.    0.    0.247 0.753]\n",
      "dance [0.    0.    0.    0.257 0.743]\n",
      "dance [0.    0.    0.    0.218 0.782]\n",
      "dance [0.    0.    0.    0.205 0.794]\n",
      "dance [0.    0.    0.    0.183 0.817]\n",
      "dance [0.   0.   0.   0.13 0.87]\n",
      "dance [0.    0.    0.    0.162 0.838]\n",
      "dance [0.    0.    0.    0.188 0.811]\n",
      "dance [0.    0.    0.    0.189 0.811]\n",
      "dance [0.    0.    0.    0.195 0.805]\n",
      "dance [0.    0.    0.    0.189 0.811]\n",
      "dance [0.    0.    0.    0.167 0.833]\n",
      "dance [0.    0.    0.    0.138 0.862]\n",
      "dance [0.    0.    0.    0.132 0.868]\n",
      "dance [0.    0.    0.    0.152 0.848]\n",
      "dance [0.    0.    0.    0.134 0.866]\n",
      "dance [0.    0.    0.    0.125 0.875]\n",
      "dance [0.    0.    0.    0.128 0.872]\n",
      "dance [0.    0.    0.    0.114 0.886]\n",
      "dance [0.    0.    0.    0.098 0.901]\n",
      "dance [0.    0.    0.    0.097 0.902]\n",
      "dance [0.    0.    0.    0.102 0.898]\n",
      "dance [0.    0.    0.    0.089 0.911]\n",
      "dance [0.    0.    0.    0.067 0.932]\n",
      "dance [0.    0.    0.001 0.051 0.948]\n",
      "dance [0.    0.    0.001 0.033 0.966]\n",
      "dance [0.    0.    0.004 0.044 0.951]\n",
      "dance [0.    0.    0.006 0.021 0.973]\n",
      "dance [0.    0.    0.011 0.008 0.981]\n",
      "dance [0.    0.    0.005 0.011 0.984]\n",
      "dance [0.    0.    0.002 0.007 0.991]\n",
      "dance [0.    0.    0.002 0.004 0.994]\n",
      "dance [0.    0.    0.001 0.004 0.995]\n",
      "dance [0.    0.    0.001 0.003 0.995]\n",
      "dance [0.    0.    0.001 0.003 0.996]\n",
      "dance [0.    0.    0.003 0.004 0.993]\n",
      "dance [0.    0.    0.004 0.003 0.993]\n",
      "dance [0.    0.    0.006 0.002 0.992]\n",
      "dance [0.    0.    0.008 0.003 0.989]\n",
      "dance [0.    0.    0.01  0.002 0.988]\n",
      "dance [0.    0.    0.011 0.002 0.987]\n",
      "dance [0.    0.    0.015 0.001 0.984]\n",
      "dance [0.    0.    0.019 0.001 0.981]\n",
      "dance [0.    0.    0.025 0.001 0.975]\n",
      "dance [0.    0.    0.042 0.    0.958]\n",
      "dance [0.    0.    0.074 0.    0.926]\n",
      "dance [0.    0.    0.117 0.    0.883]\n",
      "dance [0.    0.    0.098 0.    0.901]\n",
      "dance [0.    0.    0.113 0.    0.886]\n",
      "dance [0.    0.    0.127 0.    0.872]\n",
      "dance [0.    0.    0.21  0.    0.789]\n",
      "dance [0.    0.    0.292 0.    0.708]\n",
      "M [0.    0.    0.504 0.    0.496]\n",
      "M [0.    0.    0.846 0.    0.154]\n",
      "M [0.    0.    0.934 0.001 0.065]\n",
      "M [0.    0.    0.993 0.001 0.006]\n",
      "M [0.    0.    0.986 0.005 0.009]\n",
      "M [0.    0.    0.95  0.023 0.027]\n",
      "M [0.    0.    0.615 0.118 0.267]\n",
      "M [0.    0.    0.52  0.126 0.354]\n",
      "M [0.    0.    0.509 0.106 0.385]\n",
      "M [0.    0.    0.526 0.104 0.37 ]\n",
      "M [0.    0.    0.56  0.086 0.353]\n",
      "M [0.    0.    0.554 0.069 0.376]\n",
      "M [0.    0.    0.581 0.062 0.358]\n",
      "M [0.    0.    0.587 0.066 0.347]\n",
      "M [0.    0.    0.598 0.067 0.335]\n",
      "M [0.    0.    0.605 0.067 0.327]\n",
      "M [0.    0.    0.636 0.076 0.289]\n",
      "M [0.    0.    0.688 0.087 0.224]\n",
      "M [0.    0.    0.694 0.09  0.216]\n",
      "M [0.    0.    0.709 0.085 0.207]\n",
      "M [0.    0.    0.759 0.124 0.117]\n",
      "M [0.    0.    0.827 0.096 0.078]\n",
      "M [0.    0.    0.893 0.022 0.086]\n",
      "M [0.    0.    0.861 0.006 0.133]\n",
      "dance [0.    0.    0.45  0.012 0.537]\n",
      "dance [0.    0.    0.28  0.007 0.712]\n",
      "dance [0.    0.    0.115 0.005 0.88 ]\n",
      "dance [0.    0.    0.097 0.003 0.9  ]\n",
      "dance [0.    0.    0.109 0.003 0.888]\n",
      "dance [0.    0.    0.139 0.004 0.857]\n",
      "dance [0.    0.    0.066 0.004 0.929]\n",
      "dance [0.    0.    0.056 0.007 0.937]\n",
      "dance [0.    0.    0.069 0.006 0.924]\n",
      "dance [0.    0.    0.082 0.005 0.912]\n",
      "dance [0.    0.    0.098 0.005 0.897]\n",
      "dance [0.    0.    0.091 0.005 0.904]\n",
      "dance [0.    0.    0.069 0.004 0.927]\n",
      "dance [0.    0.    0.05  0.003 0.946]\n",
      "dance [0.    0.    0.063 0.004 0.933]\n",
      "dance [0.    0.    0.059 0.004 0.938]\n",
      "dance [0.    0.    0.063 0.003 0.935]\n",
      "dance [0.    0.    0.065 0.003 0.931]\n",
      "dance [0.    0.    0.069 0.005 0.926]\n",
      "dance [0.    0.    0.067 0.003 0.93 ]\n",
      "dance [0.    0.    0.046 0.007 0.948]\n",
      "dance [0.    0.    0.029 0.004 0.967]\n",
      "dance [0.    0.    0.01  0.007 0.983]\n",
      "dance [0.    0.    0.003 0.003 0.994]\n",
      "dance [0.    0.    0.004 0.003 0.993]\n",
      "dance [0.    0.    0.003 0.003 0.994]\n",
      "dance [0.    0.    0.004 0.005 0.991]\n",
      "dance [0.    0.    0.005 0.021 0.974]\n",
      "dance [0.    0.    0.003 0.021 0.976]\n",
      "dance [0.    0.    0.003 0.019 0.977]\n",
      "dance [0.    0.    0.005 0.126 0.868]\n",
      "dance [0.    0.    0.008 0.337 0.655]\n",
      "dance [0.    0.    0.017 0.123 0.86 ]\n",
      "dance [0.    0.    0.014 0.049 0.937]\n",
      "dance [0.    0.    0.018 0.062 0.92 ]\n",
      "dance [0.    0.    0.026 0.032 0.941]\n",
      "dance [0.    0.    0.026 0.032 0.942]\n",
      "dance [0.    0.    0.052 0.07  0.878]\n",
      "dance [0.    0.    0.03  0.213 0.757]\n",
      "dance [0.    0.    0.022 0.334 0.644]\n",
      "dance [0.    0.    0.022 0.277 0.701]\n",
      "dance [0.    0.    0.014 0.132 0.854]\n",
      "dance [0.    0.    0.021 0.148 0.831]\n",
      "dance [0.   0.   0.04 0.18 0.78]\n",
      "dance [0.    0.    0.336 0.1   0.564]\n",
      "M [0.    0.    0.488 0.235 0.277]\n",
      "Y [0.   0.   0.2  0.64 0.16]\n",
      "Y [0.    0.    0.041 0.899 0.06 ]\n",
      "Y [0.    0.    0.012 0.948 0.039]\n",
      "Y [0.    0.    0.006 0.951 0.043]\n",
      "Y [0.    0.    0.002 0.968 0.03 ]\n",
      "Y [0.    0.    0.001 0.973 0.026]\n",
      "Y [0.    0.    0.001 0.956 0.043]\n",
      "Y [0.    0.    0.    0.815 0.185]\n",
      "Y [0.    0.    0.    0.539 0.461]\n",
      "dance [0.    0.    0.001 0.194 0.805]\n",
      "dance [0.    0.    0.    0.002 0.997]\n",
      "dance [0.    0.    0.    0.005 0.995]\n",
      "dance [0.    0.    0.    0.005 0.995]\n",
      "dance [0.    0.    0.    0.011 0.989]\n",
      "dance [0.    0.    0.    0.022 0.978]\n",
      "dance [0.    0.    0.    0.019 0.981]\n",
      "dance [0.    0.    0.001 0.027 0.973]\n",
      "dance [0.    0.    0.001 0.019 0.979]\n",
      "dance [0.    0.    0.001 0.012 0.987]\n",
      "dance [0.    0.    0.001 0.021 0.979]\n",
      "dance [0.    0.    0.    0.011 0.989]\n",
      "dance [0.    0.    0.    0.007 0.992]\n",
      "dance [0.    0.    0.001 0.001 0.998]\n",
      "dance [0.    0.    0.002 0.001 0.997]\n",
      "dance [0.    0.    0.005 0.002 0.993]\n",
      "dance [0.    0.    0.013 0.006 0.981]\n",
      "dance [0.    0.    0.008 0.096 0.896]\n",
      "dance [0.    0.    0.002 0.141 0.857]\n",
      "dance [0.    0.    0.    0.127 0.873]\n",
      "dance [0.    0.    0.    0.093 0.907]\n",
      "dance [0.    0.    0.    0.064 0.936]\n",
      "dance [0.    0.    0.    0.072 0.928]\n",
      "dance [0.    0.    0.    0.101 0.898]\n",
      "dance [0.    0.    0.    0.081 0.919]\n",
      "dance [0.    0.    0.    0.054 0.946]\n",
      "dance [0.    0.    0.    0.039 0.96 ]\n",
      "dance [0.    0.    0.    0.022 0.977]\n",
      "dance [0.    0.    0.    0.031 0.969]\n",
      "dance [0.    0.    0.    0.078 0.922]\n",
      "dance [0.    0.    0.    0.156 0.844]\n",
      "dance [0.    0.    0.001 0.26  0.739]\n",
      "dance [0.    0.    0.    0.012 0.987]\n",
      "dance [0.    0.    0.001 0.005 0.995]\n",
      "dance [0.    0.    0.001 0.008 0.991]\n",
      "dance [0.    0.    0.001 0.005 0.994]\n",
      "dance [0.    0.    0.001 0.003 0.997]\n",
      "dance [0.    0.    0.    0.004 0.996]\n",
      "dance [0.    0.    0.    0.004 0.996]\n",
      "dance [0.    0.    0.    0.005 0.994]\n",
      "dance [0.    0.    0.    0.014 0.986]\n",
      "dance [0.    0.    0.    0.038 0.962]\n",
      "dance [0.    0.    0.    0.122 0.878]\n",
      "dance [0.    0.    0.    0.468 0.532]\n",
      "Y [0.    0.    0.    0.513 0.487]\n",
      "dance [0.    0.    0.    0.368 0.632]\n",
      "dance [0.    0.    0.    0.29  0.709]\n",
      "dance [0.    0.    0.    0.235 0.764]\n",
      "dance [0.    0.    0.    0.289 0.711]\n",
      "dance [0.    0.    0.    0.389 0.611]\n",
      "dance [0.    0.    0.    0.434 0.566]\n",
      "Y [0.    0.    0.    0.504 0.496]\n",
      "Y [0.    0.    0.    0.537 0.463]\n",
      "dance [0.    0.    0.    0.409 0.59 ]\n",
      "dance [0.    0.    0.    0.325 0.675]\n",
      "dance [0.    0.    0.    0.179 0.821]\n",
      "dance [0.    0.    0.    0.101 0.899]\n",
      "dance [0.    0.    0.    0.067 0.933]\n",
      "dance [0.    0.    0.    0.033 0.966]\n",
      "dance [0.    0.    0.    0.113 0.887]\n",
      "dance [0.    0.    0.    0.122 0.878]\n",
      "dance [0.    0.    0.    0.169 0.83 ]\n",
      "dance [0.    0.    0.    0.169 0.83 ]\n",
      "dance [0.    0.    0.    0.181 0.818]\n",
      "dance [0.    0.    0.    0.085 0.914]\n",
      "dance [0.    0.    0.    0.113 0.887]\n",
      "dance [0.    0.    0.    0.091 0.909]\n",
      "dance [0.    0.    0.    0.066 0.934]\n",
      "dance [0.    0.    0.    0.063 0.936]\n",
      "dance [0.    0.    0.    0.055 0.945]\n",
      "dance [0.    0.    0.    0.057 0.943]\n",
      "dance [0.    0.    0.    0.054 0.946]\n",
      "dance [0.   0.   0.   0.07 0.93]\n",
      "dance [0.    0.    0.    0.297 0.702]\n",
      "Y [0.    0.    0.001 0.707 0.292]\n",
      "Y [0.    0.    0.002 0.978 0.02 ]\n",
      "Y [0.    0.    0.001 0.998 0.001]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.001 0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.001 0.998 0.   ]\n",
      "Y [0.    0.    0.001 0.998 0.   ]\n",
      "Y [0.001 0.    0.003 0.997 0.   ]\n",
      "Y [0.001 0.    0.004 0.994 0.   ]\n",
      "Y [0.001 0.    0.002 0.997 0.   ]\n",
      "Y [0.002 0.    0.002 0.996 0.   ]\n",
      "Y [0.005 0.    0.006 0.989 0.   ]\n",
      "Y [0.007 0.    0.01  0.983 0.   ]\n",
      "Y [0.015 0.    0.015 0.97  0.   ]\n",
      "Y [0.011 0.    0.013 0.976 0.   ]\n",
      "Y [0.036 0.    0.019 0.946 0.   ]\n",
      "Y [0.054 0.    0.021 0.925 0.   ]\n",
      "Y [0.06  0.    0.022 0.919 0.   ]\n",
      "Y [0.048 0.    0.02  0.932 0.   ]\n",
      "Y [0.07  0.    0.019 0.911 0.   ]\n",
      "Y [0.062 0.    0.016 0.922 0.   ]\n",
      "Y [0.037 0.    0.014 0.948 0.   ]\n",
      "Y [0.04  0.    0.012 0.949 0.   ]\n",
      "Y [0.025 0.    0.009 0.966 0.   ]\n",
      "Y [0.024 0.    0.009 0.967 0.   ]\n",
      "Y [0.037 0.    0.012 0.951 0.   ]\n",
      "Y [0.043 0.    0.013 0.945 0.   ]\n",
      "Y [0.038 0.    0.013 0.949 0.   ]\n",
      "Y [0.037 0.    0.016 0.947 0.   ]\n",
      "Y [0.023 0.    0.023 0.954 0.   ]\n",
      "Y [0.033 0.    0.02  0.947 0.   ]\n",
      "Y [0.02  0.    0.015 0.965 0.   ]\n",
      "Y [0.019 0.    0.021 0.96  0.   ]\n",
      "Y [0.048 0.    0.03  0.922 0.   ]\n",
      "Y [0.158 0.    0.039 0.803 0.   ]\n",
      "Y [0.236 0.    0.042 0.722 0.   ]\n",
      "Y [0.125 0.    0.023 0.852 0.   ]\n",
      "Y [0.136 0.    0.02  0.844 0.   ]\n",
      "Y [0.314 0.    0.031 0.655 0.   ]\n",
      "Y [0.298 0.    0.02  0.682 0.   ]\n",
      "Y [0.271 0.    0.018 0.712 0.   ]\n",
      "Y [0.272 0.    0.023 0.705 0.   ]\n",
      "Y [0.264 0.    0.021 0.715 0.   ]\n",
      "Y [0.206 0.    0.022 0.772 0.   ]\n",
      "Y [0.176 0.    0.021 0.803 0.   ]\n",
      "Y [0.17  0.    0.021 0.809 0.   ]\n",
      "Y [0.106 0.    0.024 0.87  0.   ]\n",
      "Y [0.091 0.    0.019 0.89  0.   ]\n",
      "Y [0.087 0.    0.02  0.893 0.   ]\n",
      "Y [0.095 0.    0.021 0.885 0.   ]\n",
      "Y [0.088 0.    0.024 0.888 0.   ]\n",
      "Y [0.096 0.    0.022 0.882 0.   ]\n",
      "Y [0.079 0.    0.018 0.902 0.   ]\n",
      "Y [0.085 0.    0.024 0.891 0.   ]\n",
      "Y [0.107 0.    0.028 0.865 0.   ]\n",
      "Y [0.153 0.    0.026 0.82  0.   ]\n",
      "Y [0.133 0.    0.026 0.841 0.   ]\n",
      "Y [0.137 0.    0.019 0.843 0.   ]\n",
      "Y [0.092 0.    0.016 0.891 0.   ]\n",
      "Y [0.144 0.    0.018 0.838 0.   ]\n",
      "Y [0.16  0.    0.018 0.822 0.   ]\n",
      "Y [0.139 0.    0.019 0.842 0.   ]\n",
      "Y [0.069 0.    0.01  0.921 0.   ]\n",
      "Y [0.069 0.    0.01  0.921 0.   ]\n",
      "Y [0.08 0.   0.01 0.91 0.  ]\n",
      "Y [0.154 0.    0.015 0.831 0.   ]\n",
      "Y [0.128 0.    0.013 0.859 0.   ]\n",
      "Y [0.099 0.    0.012 0.889 0.   ]\n",
      "Y [0.119 0.    0.016 0.865 0.   ]\n",
      "Y [0.125 0.    0.019 0.856 0.   ]\n",
      "Y [0.12  0.    0.019 0.861 0.   ]\n",
      "Y [0.17  0.    0.018 0.812 0.   ]\n",
      "Y [0.239 0.    0.019 0.742 0.   ]\n",
      "Y [0.162 0.    0.013 0.825 0.   ]\n",
      "Y [0.143 0.    0.014 0.843 0.   ]\n",
      "Y [0.081 0.    0.01  0.909 0.   ]\n",
      "Y [0.06  0.    0.008 0.932 0.   ]\n",
      "Y [0.029 0.    0.005 0.966 0.   ]\n",
      "Y [0.039 0.    0.007 0.954 0.   ]\n",
      "Y [0.051 0.    0.008 0.941 0.   ]\n",
      "Y [0.131 0.    0.013 0.857 0.   ]\n",
      "Y [0.128 0.    0.011 0.861 0.   ]\n",
      "Y [0.078 0.    0.01  0.912 0.   ]\n",
      "Y [0.099 0.    0.012 0.889 0.   ]\n",
      "Y [0.119 0.    0.01  0.87  0.   ]\n",
      "Y [0.123 0.    0.011 0.866 0.   ]\n",
      "Y [0.13  0.    0.011 0.859 0.   ]\n",
      "Y [0.133 0.    0.011 0.856 0.   ]\n",
      "Y [0.122 0.    0.01  0.868 0.   ]\n",
      "Y [0.123 0.    0.009 0.868 0.   ]\n",
      "Y [0.111 0.    0.009 0.88  0.   ]\n",
      "Y [0.092 0.    0.008 0.899 0.   ]\n",
      "Y [0.126 0.    0.008 0.866 0.   ]\n",
      "Y [0.133 0.    0.008 0.858 0.   ]\n",
      "Y [0.115 0.    0.008 0.877 0.   ]\n",
      "Y [0.121 0.    0.009 0.871 0.   ]\n",
      "Y [0.153 0.    0.01  0.837 0.   ]\n",
      "Y [0.16  0.    0.01  0.829 0.   ]\n",
      "Y [0.156 0.    0.011 0.833 0.   ]\n",
      "Y [0.154 0.    0.011 0.835 0.   ]\n",
      "Y [0.13  0.    0.011 0.86  0.   ]\n",
      "Y [0.171 0.    0.014 0.816 0.   ]\n",
      "Y [0.162 0.    0.013 0.826 0.   ]\n",
      "Y [0.099 0.    0.011 0.891 0.   ]\n",
      "Y [0.07  0.    0.007 0.923 0.   ]\n",
      "Y [0.083 0.    0.012 0.905 0.   ]\n",
      "Y [0.131 0.    0.012 0.857 0.   ]\n",
      "Y [0.252 0.    0.016 0.732 0.   ]\n",
      "Y [0.284 0.    0.013 0.703 0.   ]\n",
      "Y [0.235 0.    0.011 0.754 0.   ]\n",
      "Y [0.211 0.    0.009 0.78  0.   ]\n",
      "Y [0.161 0.    0.007 0.832 0.   ]\n",
      "Y [0.106 0.    0.006 0.888 0.   ]\n",
      "Y [0.123 0.    0.007 0.87  0.   ]\n",
      "Y [0.178 0.    0.01  0.812 0.   ]\n",
      "Y [0.187 0.    0.009 0.804 0.   ]\n",
      "Y [0.132 0.    0.008 0.861 0.   ]\n",
      "Y [0.135 0.    0.007 0.857 0.   ]\n",
      "Y [0.21  0.    0.013 0.777 0.   ]\n",
      "A [0.666 0.    0.014 0.32  0.   ]\n",
      "A [0.67  0.    0.014 0.316 0.   ]\n",
      "A [0.583 0.    0.016 0.401 0.   ]\n",
      "Y [0.447 0.    0.021 0.532 0.   ]\n",
      "Y [0.429 0.    0.024 0.548 0.   ]\n",
      "Y [0.422 0.    0.019 0.559 0.   ]\n",
      "Y [0.408 0.    0.014 0.578 0.   ]\n",
      "Y [0.438 0.    0.016 0.546 0.   ]\n",
      "Y [0.448 0.    0.015 0.536 0.   ]\n",
      "Y [0.441 0.    0.015 0.544 0.   ]\n",
      "Y [0.479 0.    0.016 0.504 0.   ]\n",
      "A [0.517 0.    0.014 0.469 0.   ]\n",
      "A [0.544 0.    0.018 0.437 0.   ]\n",
      "Y [0.453 0.    0.017 0.53  0.   ]\n",
      "A [0.566 0.    0.017 0.417 0.   ]\n",
      "A [0.636 0.    0.013 0.351 0.   ]\n",
      "A [0.73  0.    0.011 0.26  0.   ]\n",
      "A [0.782 0.    0.009 0.208 0.   ]\n",
      "Y [0.486 0.    0.011 0.503 0.   ]\n",
      "Y [0.274 0.    0.007 0.719 0.   ]\n",
      "Y [0.221 0.    0.006 0.774 0.   ]\n",
      "Y [0.238 0.    0.005 0.757 0.   ]\n",
      "Y [0.186 0.    0.004 0.81  0.   ]\n",
      "Y [0.169 0.    0.006 0.825 0.   ]\n",
      "Y [0.168 0.    0.007 0.825 0.   ]\n",
      "Y [0.183 0.    0.007 0.809 0.   ]\n",
      "Y [0.201 0.    0.008 0.791 0.   ]\n",
      "Y [0.166 0.    0.01  0.824 0.   ]\n",
      "Y [0.1   0.    0.007 0.893 0.   ]\n",
      "Y [0.09  0.    0.008 0.901 0.   ]\n",
      "Y [0.261 0.    0.032 0.707 0.   ]\n",
      "Y [0.372 0.    0.035 0.593 0.   ]\n",
      "A [0.518 0.    0.058 0.424 0.   ]\n",
      "A [0.78 0.   0.06 0.16 0.  ]\n",
      "A [0.685 0.    0.118 0.197 0.   ]\n",
      "A [0.564 0.    0.136 0.299 0.   ]\n",
      "Y [0.361 0.    0.205 0.433 0.   ]\n",
      "A [0.466 0.    0.237 0.297 0.   ]\n",
      "A [0.529 0.    0.239 0.232 0.   ]\n",
      "A [0.707 0.    0.209 0.084 0.   ]\n",
      "M [0.393 0.    0.478 0.129 0.   ]\n",
      "M [0.086 0.    0.732 0.181 0.   ]\n",
      "Y [0.    0.    0.015 0.984 0.   ]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.    0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.999 0.   ]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.995 0.005]\n",
      "Y [0.    0.    0.002 0.804 0.194]\n",
      "dance [0.    0.    0.011 0.054 0.935]\n",
      "dance [0.    0.001 0.081 0.009 0.909]\n",
      "dance [0.002 0.088 0.183 0.011 0.716]\n",
      "dance [0.003 0.398 0.069 0.015 0.514]\n",
      "C [0.001 0.808 0.025 0.002 0.164]\n",
      "C [0.001 0.926 0.006 0.    0.067]\n",
      "C [0.002 0.91  0.006 0.    0.082]\n",
      "A [0.997 0.002 0.001 0.    0.   ]\n",
      "A [0.82  0.114 0.02  0.001 0.046]\n",
      "dance [0.001 0.472 0.015 0.001 0.511]\n",
      "C [0.001 0.499 0.007 0.    0.493]\n",
      "C [0.    0.782 0.044 0.    0.174]\n",
      "C [0.    0.465 0.138 0.004 0.393]\n",
      "dance [0.    0.142 0.173 0.121 0.562]\n",
      "Y [0.    0.005 0.184 0.758 0.053]\n",
      "Y [0.    0.    0.014 0.978 0.007]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.001]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.006 0.994 0.   ]\n",
      "Y [0.    0.    0.001 0.998 0.001]\n",
      "Y [0.    0.    0.002 0.996 0.001]\n",
      "Y [0.    0.    0.003 0.995 0.002]\n",
      "Y [0.    0.    0.006 0.99  0.004]\n",
      "Y [0.    0.    0.01  0.982 0.009]\n",
      "Y [0.    0.    0.011 0.977 0.012]\n",
      "Y [0.    0.    0.012 0.975 0.012]\n",
      "Y [0.    0.001 0.016 0.968 0.016]\n",
      "Y [0.    0.001 0.017 0.967 0.016]\n",
      "Y [0.    0.001 0.021 0.963 0.016]\n",
      "Y [0.    0.001 0.019 0.966 0.014]\n",
      "Y [0.    0.001 0.021 0.966 0.012]\n",
      "Y [0.    0.001 0.021 0.966 0.012]\n",
      "Y [0.    0.001 0.021 0.968 0.011]\n",
      "Y [0.    0.001 0.02  0.97  0.01 ]\n",
      "Y [0.    0.001 0.014 0.978 0.007]\n",
      "Y [0.    0.    0.014 0.979 0.007]\n",
      "Y [0.    0.    0.013 0.981 0.006]\n",
      "Y [0.    0.    0.01  0.984 0.005]\n",
      "Y [0.    0.    0.01  0.985 0.005]\n",
      "Y [0.    0.    0.008 0.986 0.005]\n",
      "Y [0.    0.    0.008 0.987 0.005]\n",
      "Y [0.    0.    0.01  0.983 0.006]\n",
      "Y [0.    0.    0.011 0.982 0.007]\n",
      "Y [0.    0.    0.011 0.982 0.007]\n",
      "Y [0.    0.    0.006 0.988 0.005]\n",
      "Y [0.    0.    0.001 0.997 0.001]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.007 0.993 0.   ]\n",
      "Y [0.    0.    0.028 0.971 0.   ]\n",
      "Y [0.    0.    0.062 0.937 0.   ]\n",
      "Y [0.    0.    0.121 0.878 0.   ]\n",
      "Y [0.001 0.    0.127 0.872 0.   ]\n",
      "Y [0.002 0.    0.307 0.69  0.001]\n",
      "Y [0.   0.   0.01 0.99 0.  ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.006 0.994 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.004 0.995 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.003 0.996 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.003 0.996 0.   ]\n",
      "Y [0.    0.    0.003 0.996 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.998 0.   ]\n",
      "Y [0.    0.    0.001 0.998 0.   ]\n",
      "Y [0.    0.    0.001 0.998 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.002 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.003 0.997 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.006 0.994 0.   ]\n",
      "Y [0.    0.    0.008 0.992 0.   ]\n",
      "Y [0.    0.    0.005 0.995 0.   ]\n",
      "Y [0.    0.    0.005 0.995 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.006 0.994 0.   ]\n",
      "Y [0.    0.    0.007 0.992 0.   ]\n",
      "Y [0.    0.    0.011 0.988 0.   ]\n",
      "Y [0.    0.    0.021 0.978 0.001]\n",
      "Y [0.    0.    0.024 0.975 0.001]\n",
      "Y [0.    0.    0.016 0.983 0.001]\n",
      "Y [0.    0.    0.015 0.985 0.001]\n",
      "Y [0.    0.    0.02  0.979 0.001]\n",
      "Y [0.    0.    0.033 0.965 0.001]\n",
      "Y [0.    0.    0.026 0.973 0.001]\n",
      "Y [0.    0.    0.021 0.978 0.001]\n",
      "Y [0.    0.    0.017 0.982 0.001]\n",
      "Y [0.    0.    0.015 0.985 0.001]\n",
      "Y [0.    0.    0.013 0.986 0.001]\n",
      "Y [0.    0.    0.014 0.986 0.001]\n",
      "Y [0.    0.    0.012 0.987 0.001]\n",
      "Y [0.    0.    0.009 0.99  0.   ]\n",
      "Y [0.    0.    0.01  0.989 0.   ]\n",
      "Y [0.    0.    0.009 0.99  0.   ]\n",
      "Y [0.    0.    0.012 0.987 0.   ]\n",
      "Y [0.    0.    0.018 0.981 0.001]\n",
      "Y [0.    0.    0.026 0.974 0.001]\n",
      "Y [0.    0.    0.036 0.963 0.001]\n",
      "Y [0.    0.    0.033 0.966 0.001]\n",
      "Y [0.    0.    0.033 0.966 0.001]\n",
      "Y [0.    0.    0.033 0.967 0.001]\n",
      "Y [0.    0.    0.04  0.959 0.001]\n",
      "Y [0.    0.    0.03  0.969 0.001]\n",
      "Y [0.    0.    0.029 0.97  0.001]\n",
      "Y [0.    0.    0.027 0.972 0.001]\n",
      "Y [0.    0.    0.03  0.969 0.001]\n",
      "Y [0.    0.    0.044 0.955 0.001]\n",
      "Y [0.    0.    0.051 0.948 0.001]\n",
      "Y [0.    0.    0.05  0.949 0.001]\n",
      "Y [0.    0.    0.039 0.96  0.001]\n",
      "Y [0.    0.    0.023 0.976 0.001]\n",
      "Y [0.    0.    0.014 0.985 0.001]\n",
      "Y [0.    0.    0.013 0.986 0.001]\n",
      "Y [0.    0.    0.013 0.986 0.001]\n",
      "Y [0.    0.    0.012 0.987 0.001]\n",
      "Y [0.    0.    0.01  0.989 0.   ]\n",
      "Y [0.    0.    0.01  0.989 0.001]\n",
      "Y [0.    0.    0.01  0.989 0.   ]\n",
      "Y [0.    0.    0.01  0.989 0.   ]\n",
      "Y [0.    0.    0.011 0.988 0.001]\n",
      "Y [0.    0.    0.011 0.988 0.001]\n",
      "Y [0.    0.    0.015 0.984 0.001]\n",
      "Y [0.    0.    0.015 0.984 0.001]\n",
      "Y [0.    0.    0.013 0.986 0.001]\n",
      "Y [0.    0.    0.012 0.988 0.   ]\n",
      "Y [0.   0.   0.01 0.99 0.  ]\n",
      "Y [0.    0.    0.016 0.983 0.   ]\n",
      "Y [0.    0.    0.019 0.981 0.001]\n",
      "Y [0.    0.    0.021 0.978 0.001]\n",
      "Y [0.    0.    0.019 0.98  0.001]\n",
      "Y [0.    0.    0.021 0.978 0.001]\n",
      "Y [0.    0.    0.02  0.98  0.001]\n",
      "Y [0.    0.    0.025 0.974 0.001]\n",
      "Y [0.    0.    0.052 0.947 0.   ]\n",
      "Y [0.    0.    0.021 0.979 0.   ]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0.001 0.    0.004 0.995 0.   ]\n",
      "Y [0.001 0.    0.005 0.994 0.   ]\n",
      "Y [0.001 0.    0.004 0.995 0.   ]\n",
      "Y [0.001 0.    0.005 0.994 0.   ]\n",
      "Y [0.002 0.    0.005 0.993 0.   ]\n",
      "Y [0.001 0.    0.005 0.993 0.   ]\n",
      "Y [0.002 0.    0.003 0.995 0.   ]\n",
      "Y [0.003 0.    0.007 0.99  0.   ]\n",
      "Y [0.006 0.    0.008 0.986 0.   ]\n",
      "Y [0.009 0.    0.008 0.983 0.   ]\n",
      "Y [0.01  0.    0.013 0.976 0.   ]\n",
      "Y [0.023 0.    0.027 0.949 0.   ]\n",
      "Y [0.018 0.    0.027 0.955 0.   ]\n",
      "Y [0.013 0.    0.017 0.969 0.   ]\n",
      "Y [0.017 0.    0.019 0.964 0.   ]\n",
      "Y [0.032 0.    0.016 0.951 0.   ]\n",
      "Y [0.031 0.    0.018 0.952 0.   ]\n",
      "Y [0.043 0.    0.02  0.937 0.   ]\n",
      "Y [0.036 0.    0.017 0.947 0.   ]\n",
      "Y [0.043 0.    0.018 0.94  0.   ]\n",
      "Y [0.036 0.    0.018 0.946 0.   ]\n",
      "Y [0.072 0.    0.029 0.899 0.   ]\n",
      "Y [0.145 0.    0.034 0.821 0.   ]\n",
      "Y [0.181 0.    0.035 0.784 0.   ]\n",
      "Y [0.199 0.    0.027 0.774 0.   ]\n",
      "Y [0.205 0.    0.028 0.768 0.   ]\n",
      "Y [0.382 0.    0.038 0.58  0.   ]\n",
      "Y [0.457 0.    0.041 0.502 0.   ]\n",
      "Y [0.474 0.    0.043 0.483 0.   ]\n",
      "A [0.482 0.    0.043 0.475 0.   ]\n",
      "A [0.486 0.    0.042 0.472 0.   ]\n",
      "Y [0.392 0.    0.051 0.556 0.   ]\n",
      "Y [0.416 0.    0.047 0.538 0.   ]\n",
      "Y [0.402 0.    0.048 0.55  0.   ]\n",
      "Y [0.388 0.    0.049 0.563 0.   ]\n",
      "Y [0.411 0.    0.045 0.544 0.   ]\n",
      "Y [0.386 0.    0.044 0.571 0.   ]\n",
      "Y [0.431 0.    0.032 0.537 0.   ]\n",
      "Y [0.264 0.    0.052 0.684 0.   ]\n",
      "Y [0.311 0.    0.039 0.651 0.   ]\n",
      "Y [0.342 0.    0.056 0.602 0.   ]\n",
      "Y [0.379 0.    0.046 0.575 0.   ]\n",
      "Y [0.366 0.    0.03  0.604 0.   ]\n",
      "Y [0.258 0.    0.019 0.723 0.   ]\n",
      "Y [0.17 0.   0.02 0.81 0.  ]\n",
      "Y [0.152 0.    0.018 0.831 0.   ]\n",
      "Y [0.181 0.    0.019 0.8   0.   ]\n",
      "Y [0.209 0.    0.018 0.773 0.   ]\n",
      "Y [0.199 0.    0.021 0.781 0.   ]\n",
      "Y [0.22  0.    0.021 0.76  0.   ]\n",
      "Y [0.201 0.    0.024 0.776 0.   ]\n",
      "Y [0.196 0.    0.027 0.777 0.   ]\n",
      "Y [0.181 0.    0.026 0.793 0.   ]\n",
      "Y [0.253 0.    0.022 0.725 0.   ]\n",
      "Y [0.31  0.    0.023 0.666 0.   ]\n",
      "Y [0.216 0.    0.023 0.761 0.   ]\n",
      "Y [0.195 0.    0.019 0.787 0.   ]\n",
      "Y [0.209 0.    0.023 0.769 0.   ]\n",
      "Y [0.348 0.    0.024 0.628 0.   ]\n",
      "Y [0.451 0.    0.025 0.524 0.   ]\n",
      "A [0.522 0.    0.028 0.45  0.   ]\n",
      "Y [0.453 0.    0.032 0.515 0.   ]\n",
      "Y [0.318 0.    0.03  0.652 0.   ]\n",
      "Y [0.197 0.    0.03  0.774 0.   ]\n",
      "Y [0.175 0.    0.027 0.798 0.   ]\n",
      "Y [0.144 0.    0.02  0.835 0.   ]\n",
      "Y [0.139 0.    0.019 0.842 0.   ]\n",
      "Y [0.171 0.    0.013 0.816 0.   ]\n",
      "Y [0.171 0.    0.014 0.815 0.   ]\n",
      "Y [0.181 0.    0.017 0.803 0.   ]\n",
      "Y [0.209 0.    0.014 0.776 0.   ]\n",
      "A [0.534 0.    0.014 0.452 0.   ]\n",
      "Y [0.48  0.    0.015 0.505 0.   ]\n",
      "Y [0.451 0.    0.016 0.533 0.   ]\n",
      "A [0.506 0.    0.012 0.482 0.   ]\n",
      "A [0.54  0.    0.015 0.445 0.   ]\n",
      "A [0.535 0.    0.014 0.451 0.   ]\n",
      "Y [0.478 0.    0.016 0.506 0.   ]\n",
      "A [0.584 0.    0.014 0.402 0.   ]\n",
      "A [0.803 0.    0.015 0.182 0.   ]\n",
      "A [0.769 0.    0.016 0.215 0.   ]\n",
      "A [0.732 0.    0.02  0.248 0.   ]\n",
      "A [0.735 0.    0.018 0.247 0.   ]\n",
      "A [0.75  0.    0.015 0.235 0.   ]\n",
      "A [0.868 0.    0.018 0.114 0.   ]\n",
      "A [0.839 0.    0.023 0.138 0.   ]\n",
      "A [0.85  0.    0.019 0.131 0.   ]\n",
      "A [0.939 0.    0.01  0.051 0.   ]\n",
      "A [0.954 0.    0.009 0.037 0.   ]\n",
      "A [0.965 0.    0.008 0.027 0.   ]\n",
      "A [0.98  0.    0.006 0.014 0.   ]\n",
      "A [0.983 0.    0.006 0.011 0.   ]\n",
      "A [0.984 0.    0.005 0.011 0.   ]\n",
      "A [0.965 0.    0.021 0.014 0.   ]\n",
      "A [0.94  0.    0.021 0.039 0.   ]\n",
      "A [0.906 0.    0.025 0.069 0.   ]\n",
      "A [0.934 0.    0.019 0.048 0.   ]\n",
      "A [0.966 0.    0.01  0.024 0.   ]\n",
      "A [0.984 0.    0.004 0.012 0.   ]\n",
      "A [0.983 0.    0.004 0.013 0.   ]\n",
      "A [0.987 0.    0.002 0.011 0.   ]\n",
      "A [0.993 0.    0.002 0.005 0.   ]\n",
      "A [0.998 0.    0.001 0.001 0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [1. 0. 0. 0. 0.]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.999 0.    0.001 0.    0.   ]\n",
      "A [0.998 0.    0.002 0.    0.   ]\n",
      "A [0.998 0.    0.002 0.    0.   ]\n",
      "A [0.997 0.    0.003 0.    0.   ]\n",
      "A [0.994 0.    0.006 0.    0.   ]\n",
      "A [0.988 0.    0.012 0.    0.   ]\n",
      "A [0.981 0.    0.019 0.    0.   ]\n",
      "A [0.987 0.    0.013 0.    0.   ]\n",
      "A [0.993 0.    0.007 0.    0.   ]\n",
      "A [0.996 0.    0.004 0.    0.   ]\n",
      "A [0.995 0.    0.005 0.    0.   ]\n",
      "A [0.996 0.    0.004 0.    0.   ]\n",
      "A [0.997 0.    0.003 0.    0.   ]\n",
      "A [0.998 0.    0.002 0.    0.   ]\n",
      "A [0.996 0.    0.004 0.    0.   ]\n",
      "A [0.995 0.    0.005 0.    0.   ]\n",
      "A [0.994 0.    0.006 0.    0.   ]\n",
      "A [0.994 0.    0.006 0.    0.   ]\n",
      "A [0.995 0.    0.005 0.    0.   ]\n",
      "A [0.994 0.    0.006 0.    0.   ]\n",
      "A [0.996 0.    0.004 0.    0.   ]\n",
      "A [0.998 0.    0.002 0.    0.   ]\n",
      "A [0.996 0.    0.004 0.    0.   ]\n",
      "A [0.998 0.    0.002 0.    0.   ]\n",
      "A [0.997 0.    0.003 0.    0.   ]\n",
      "A [0.996 0.    0.004 0.    0.   ]\n",
      "A [0.996 0.    0.004 0.    0.   ]\n",
      "A [0.995 0.    0.005 0.    0.   ]\n",
      "A [0.995 0.    0.005 0.    0.   ]\n",
      "A [0.995 0.    0.005 0.    0.   ]\n",
      "A [0.986 0.    0.014 0.    0.   ]\n",
      "A [0.988 0.    0.012 0.    0.   ]\n",
      "A [0.799 0.    0.194 0.007 0.   ]\n",
      "M [0.019 0.    0.662 0.318 0.   ]\n",
      "Y [0.    0.    0.006 0.994 0.   ]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.001 0.999 0.   ]\n",
      "Y [0.    0.    0.018 0.981 0.   ]\n",
      "Y [0.    0.    0.131 0.868 0.001]\n",
      "M [0.    0.    0.834 0.164 0.002]\n",
      "M [0.    0.    0.927 0.073 0.   ]\n",
      "M [0.    0.    0.983 0.017 0.   ]\n",
      "M [0.    0.    0.996 0.004 0.   ]\n",
      "M [0.    0.    0.997 0.003 0.   ]\n",
      "M [0.    0.    0.999 0.001 0.   ]\n",
      "M [0.    0.    0.999 0.001 0.   ]\n",
      "M [0.    0.    0.999 0.001 0.   ]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0.    0.    0.999 0.    0.   ]\n",
      "M [0.    0.    0.999 0.001 0.   ]\n",
      "M [0.    0.    0.999 0.001 0.   ]\n",
      "M [0.    0.    0.998 0.002 0.   ]\n",
      "M [0.    0.    0.995 0.004 0.   ]\n",
      "M [0.    0.    0.988 0.011 0.   ]\n",
      "M [0.    0.    0.97  0.029 0.001]\n",
      "M [0.    0.001 0.953 0.045 0.001]\n",
      "M [0.    0.001 0.941 0.057 0.001]\n",
      "M [0.    0.001 0.942 0.056 0.001]\n",
      "M [0.    0.001 0.919 0.079 0.001]\n",
      "M [0.    0.001 0.901 0.097 0.001]\n",
      "M [0.    0.001 0.914 0.084 0.001]\n",
      "M [0.    0.002 0.922 0.076 0.001]\n",
      "M [0.    0.002 0.933 0.064 0.001]\n",
      "M [0.    0.003 0.959 0.037 0.001]\n",
      "M [0.    0.004 0.958 0.037 0.001]\n",
      "M [0.    0.003 0.955 0.041 0.001]\n",
      "M [0.    0.003 0.959 0.038 0.001]\n",
      "M [0.    0.003 0.955 0.042 0.001]\n",
      "M [0.    0.003 0.95  0.047 0.001]\n",
      "M [0.    0.003 0.95  0.046 0.001]\n",
      "M [0.    0.003 0.954 0.043 0.001]\n",
      "M [0.    0.002 0.946 0.051 0.001]\n",
      "M [0.    0.003 0.946 0.051 0.001]\n",
      "M [0.    0.003 0.946 0.05  0.001]\n",
      "M [0.    0.004 0.941 0.054 0.001]\n",
      "M [0.    0.004 0.941 0.054 0.001]\n",
      "M [0.    0.005 0.936 0.058 0.001]\n",
      "M [0.    0.005 0.936 0.058 0.001]\n",
      "M [0.    0.007 0.947 0.044 0.001]\n",
      "M [0.    0.009 0.958 0.031 0.001]\n",
      "M [0.    0.01  0.955 0.033 0.001]\n",
      "M [0.    0.012 0.949 0.038 0.002]\n",
      "M [0.    0.012 0.957 0.029 0.001]\n",
      "M [0.    0.014 0.954 0.03  0.001]\n",
      "M [0.    0.014 0.951 0.034 0.001]\n",
      "M [0.    0.014 0.943 0.041 0.001]\n",
      "M [0.    0.017 0.93  0.051 0.002]\n",
      "M [0.    0.019 0.931 0.048 0.002]\n",
      "M [0.    0.019 0.931 0.048 0.002]\n",
      "M [0.    0.021 0.942 0.035 0.002]\n",
      "M [0.    0.022 0.947 0.03  0.001]\n",
      "M [0.    0.027 0.942 0.029 0.002]\n",
      "M [0.    0.033 0.939 0.026 0.002]\n",
      "M [0.    0.043 0.937 0.018 0.002]\n",
      "M [0.    0.055 0.923 0.019 0.003]\n",
      "M [0.    0.065 0.917 0.015 0.003]\n",
      "M [0.    0.067 0.912 0.018 0.003]\n",
      "M [0.    0.065 0.914 0.019 0.002]\n",
      "M [0.    0.085 0.892 0.02  0.003]\n",
      "M [0.    0.097 0.876 0.023 0.004]\n",
      "M [0.    0.109 0.862 0.025 0.004]\n",
      "M [0.    0.13  0.838 0.026 0.006]\n",
      "M [0.    0.121 0.851 0.023 0.005]\n",
      "M [0.    0.133 0.841 0.022 0.005]\n",
      "M [0.    0.135 0.836 0.023 0.006]\n",
      "M [0.    0.137 0.836 0.022 0.005]\n",
      "M [0.    0.155 0.819 0.02  0.006]\n",
      "M [0.    0.17  0.809 0.016 0.005]\n",
      "M [0.    0.192 0.792 0.013 0.003]\n",
      "M [0.    0.238 0.744 0.015 0.003]\n",
      "M [0.    0.269 0.712 0.016 0.003]\n",
      "M [0.    0.303 0.677 0.018 0.003]\n",
      "M [0.    0.344 0.63  0.023 0.003]\n",
      "M [0.    0.391 0.572 0.033 0.003]\n",
      "M [0.    0.415 0.547 0.034 0.003]\n",
      "M [0.    0.457 0.507 0.032 0.003]\n",
      "C [0.    0.505 0.451 0.04  0.003]\n",
      "C [0.    0.548 0.415 0.033 0.003]\n",
      "C [0.    0.548 0.41  0.039 0.003]\n",
      "C [0.    0.577 0.381 0.039 0.003]\n",
      "C [0.    0.593 0.369 0.036 0.002]\n",
      "C [0.    0.618 0.355 0.024 0.002]\n",
      "C [0.    0.62  0.355 0.023 0.002]\n",
      "C [0.    0.625 0.346 0.027 0.002]\n",
      "C [0.    0.646 0.319 0.032 0.002]\n",
      "C [0.    0.658 0.303 0.036 0.002]\n",
      "C [0.    0.648 0.319 0.03  0.002]\n",
      "C [0.    0.685 0.289 0.023 0.002]\n",
      "C [0.    0.742 0.23  0.026 0.002]\n",
      "C [0.    0.767 0.208 0.024 0.002]\n",
      "C [0.    0.759 0.205 0.034 0.002]\n",
      "C [0.    0.789 0.17  0.04  0.002]\n",
      "C [0.    0.842 0.119 0.037 0.001]\n",
      "C [0.    0.885 0.081 0.034 0.001]\n",
      "C [0.    0.914 0.058 0.027 0.001]\n",
      "C [0.    0.938 0.038 0.024 0.   ]\n",
      "C [0.    0.944 0.027 0.028 0.   ]\n",
      "C [0.    0.943 0.021 0.035 0.   ]\n",
      "C [0.    0.951 0.016 0.032 0.   ]\n",
      "C [0.    0.968 0.007 0.025 0.   ]\n",
      "C [0.    0.973 0.004 0.022 0.   ]\n",
      "C [0.    0.975 0.004 0.021 0.   ]\n",
      "C [0.    0.974 0.004 0.021 0.   ]\n",
      "C [0.    0.979 0.003 0.017 0.   ]\n",
      "C [0.    0.983 0.003 0.014 0.   ]\n",
      "C [0.    0.983 0.003 0.013 0.   ]\n",
      "C [0.    0.983 0.003 0.013 0.   ]\n",
      "C [0.    0.986 0.002 0.011 0.   ]\n",
      "C [0.    0.989 0.002 0.008 0.   ]\n",
      "C [0.001 0.989 0.002 0.008 0.   ]\n",
      "C [0.    0.991 0.002 0.007 0.   ]\n",
      "C [0.    0.99  0.002 0.008 0.   ]\n",
      "C [0.001 0.989 0.002 0.008 0.   ]\n",
      "C [0.    0.988 0.003 0.008 0.   ]\n",
      "C [0.    0.988 0.004 0.008 0.   ]\n",
      "C [0.    0.986 0.004 0.009 0.   ]\n",
      "C [0.    0.987 0.004 0.009 0.   ]\n",
      "C [0.    0.987 0.003 0.009 0.   ]\n",
      "C [0.    0.986 0.003 0.01  0.   ]\n",
      "C [0.    0.984 0.004 0.012 0.   ]\n",
      "C [0.001 0.977 0.004 0.018 0.   ]\n",
      "C [0.001 0.978 0.004 0.017 0.   ]\n",
      "C [0.001 0.978 0.004 0.017 0.   ]\n",
      "C [0.001 0.982 0.004 0.013 0.   ]\n",
      "C [0.001 0.983 0.004 0.013 0.   ]\n",
      "C [0.001 0.983 0.004 0.013 0.   ]\n",
      "C [0.001 0.986 0.003 0.01  0.   ]\n",
      "C [0.001 0.986 0.003 0.01  0.   ]\n",
      "C [0.001 0.981 0.003 0.014 0.   ]\n",
      "C [0.001 0.983 0.003 0.013 0.   ]\n",
      "C [0.001 0.983 0.003 0.013 0.   ]\n",
      "C [0.002 0.981 0.003 0.014 0.   ]\n",
      "C [0.002 0.985 0.003 0.01  0.   ]\n",
      "C [0.003 0.984 0.003 0.01  0.   ]\n",
      "C [0.003 0.983 0.004 0.01  0.   ]\n",
      "C [0.002 0.985 0.003 0.009 0.   ]\n",
      "C [0.002 0.987 0.003 0.008 0.   ]\n",
      "C [0.001 0.988 0.003 0.008 0.   ]\n",
      "C [0.001 0.989 0.003 0.007 0.   ]\n",
      "C [0.001 0.987 0.003 0.009 0.   ]\n",
      "C [0.001 0.988 0.002 0.009 0.   ]\n",
      "C [0.001 0.986 0.002 0.01  0.   ]\n",
      "C [0.002 0.981 0.002 0.015 0.   ]\n",
      "C [0.002 0.977 0.002 0.019 0.   ]\n",
      "C [0.003 0.976 0.002 0.019 0.   ]\n",
      "C [0.003 0.98  0.002 0.015 0.   ]\n",
      "C [0.003 0.981 0.002 0.015 0.   ]\n",
      "C [0.003 0.98  0.002 0.015 0.   ]\n",
      "C [0.004 0.981 0.002 0.014 0.   ]\n",
      "C [0.003 0.987 0.002 0.008 0.   ]\n",
      "C [0.006 0.987 0.002 0.005 0.   ]\n",
      "C [0.011 0.983 0.001 0.004 0.   ]\n",
      "C [0.024 0.965 0.002 0.009 0.   ]\n",
      "C [0.025 0.958 0.002 0.014 0.   ]\n",
      "C [0.028 0.954 0.003 0.015 0.   ]\n",
      "C [0.018 0.959 0.008 0.015 0.   ]\n",
      "C [0.064 0.9   0.011 0.025 0.   ]\n",
      "C [0.34  0.553 0.021 0.085 0.   ]\n",
      "A [0.59  0.36  0.032 0.019 0.   ]\n",
      "A [0.65  0.253 0.092 0.005 0.   ]\n",
      "M [0.331 0.163 0.503 0.002 0.   ]\n",
      "M [0.007 0.01  0.983 0.    0.   ]\n",
      "M [0.    0.    0.999 0.    0.   ]\n",
      "M [0.    0.    0.999 0.    0.   ]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0.034 0.001 0.928 0.    0.038]\n",
      "M [0.085 0.001 0.784 0.    0.129]\n",
      "M [0.038 0.001 0.789 0.    0.171]\n",
      "M [0.005 0.    0.955 0.    0.04 ]\n",
      "M [0.01  0.    0.908 0.    0.082]\n",
      "M [0.008 0.    0.881 0.001 0.11 ]\n",
      "M [0.003 0.    0.845 0.001 0.15 ]\n",
      "M [0.005 0.    0.831 0.001 0.162]\n",
      "M [0.002 0.    0.65  0.    0.348]\n",
      "dance [0.002 0.    0.254 0.    0.744]\n",
      "dance [0.001 0.    0.2   0.    0.799]\n",
      "dance [0.    0.    0.193 0.    0.807]\n",
      "dance [0.    0.    0.144 0.    0.855]\n",
      "dance [0.    0.    0.154 0.    0.845]\n",
      "dance [0.    0.    0.121 0.    0.878]\n",
      "dance [0.    0.    0.115 0.    0.885]\n",
      "dance [0.    0.    0.128 0.    0.871]\n",
      "dance [0.    0.    0.116 0.001 0.883]\n",
      "dance [0.    0.    0.123 0.    0.876]\n",
      "dance [0.    0.    0.129 0.    0.87 ]\n",
      "dance [0.    0.    0.122 0.    0.878]\n",
      "dance [0.    0.    0.113 0.    0.886]\n",
      "dance [0.    0.    0.103 0.    0.896]\n",
      "dance [0.  0.  0.1 0.  0.9]\n",
      "dance [0.    0.    0.092 0.    0.908]\n",
      "dance [0.    0.    0.094 0.    0.906]\n",
      "dance [0.    0.    0.1   0.    0.899]\n",
      "dance [0.    0.    0.112 0.    0.887]\n",
      "dance [0.    0.    0.122 0.    0.877]\n",
      "dance [0.    0.    0.124 0.    0.876]\n",
      "dance [0.    0.    0.133 0.    0.866]\n",
      "dance [0.    0.    0.127 0.    0.873]\n",
      "dance [0.    0.    0.114 0.    0.886]\n",
      "dance [0.    0.    0.102 0.    0.897]\n",
      "dance [0.    0.    0.093 0.    0.907]\n",
      "dance [0.    0.    0.104 0.    0.895]\n",
      "dance [0.   0.   0.08 0.   0.92]\n",
      "dance [0.    0.    0.068 0.001 0.931]\n",
      "dance [0.    0.    0.079 0.001 0.92 ]\n",
      "dance [0.    0.    0.058 0.001 0.941]\n",
      "dance [0.    0.    0.058 0.001 0.941]\n",
      "dance [0.    0.    0.061 0.001 0.938]\n",
      "dance [0.    0.001 0.043 0.001 0.956]\n",
      "dance [0.    0.001 0.058 0.001 0.941]\n",
      "dance [0.    0.001 0.111 0.    0.887]\n",
      "dance [0.    0.001 0.258 0.    0.741]\n",
      "dance [0.    0.    0.356 0.    0.643]\n",
      "dance [0.    0.    0.337 0.002 0.661]\n",
      "dance [0.    0.    0.424 0.016 0.56 ]\n",
      "dance [0.    0.    0.095 0.279 0.626]\n",
      "Y [0.    0.    0.011 0.822 0.167]\n",
      "Y [0.    0.    0.002 0.926 0.072]\n",
      "Y [0.    0.    0.001 0.917 0.082]\n",
      "Y [0.    0.    0.001 0.902 0.097]\n",
      "Y [0.    0.    0.001 0.863 0.137]\n",
      "Y [0.    0.    0.001 0.92  0.079]\n",
      "Y [0.    0.    0.001 0.934 0.065]\n",
      "Y [0.    0.    0.001 0.882 0.117]\n",
      "Y [0.    0.    0.001 0.888 0.11 ]\n",
      "Y [0.    0.    0.001 0.899 0.099]\n",
      "Y [0.    0.    0.001 0.897 0.101]\n",
      "Y [0.    0.    0.001 0.668 0.331]\n",
      "Y [0.    0.    0.001 0.689 0.31 ]\n",
      "Y [0.    0.    0.001 0.573 0.425]\n",
      "dance [0.    0.    0.002 0.384 0.614]\n",
      "dance [0.    0.    0.004 0.351 0.645]\n",
      "dance [0.    0.    0.007 0.245 0.748]\n",
      "dance [0.    0.    0.008 0.072 0.92 ]\n",
      "dance [0.    0.    0.01  0.032 0.958]\n",
      "dance [0.    0.    0.048 0.007 0.945]\n",
      "dance [0.    0.    0.045 0.012 0.943]\n",
      "dance [0.    0.    0.015 0.01  0.975]\n",
      "dance [0.    0.    0.006 0.025 0.97 ]\n",
      "dance [0.    0.    0.002 0.045 0.953]\n",
      "dance [0.    0.    0.001 0.065 0.934]\n",
      "dance [0.    0.    0.001 0.069 0.93 ]\n",
      "dance [0.    0.    0.023 0.051 0.926]\n",
      "dance [0.    0.    0.496 0.    0.504]\n",
      "M [0.    0.    0.615 0.    0.385]\n",
      "M [0.    0.    0.732 0.    0.267]\n",
      "M [0.001 0.    0.809 0.008 0.182]\n",
      "Y [0.    0.    0.347 0.571 0.081]\n",
      "Y [0.    0.    0.009 0.961 0.029]\n",
      "Y [0.    0.    0.002 0.983 0.016]\n",
      "Y [0.    0.    0.001 0.975 0.024]\n",
      "Y [0.    0.    0.002 0.963 0.035]\n",
      "Y [0.    0.    0.003 0.936 0.061]\n",
      "Y [0.    0.    0.004 0.91  0.086]\n",
      "Y [0.    0.    0.006 0.891 0.103]\n",
      "Y [0.    0.    0.006 0.887 0.107]\n",
      "Y [0.    0.    0.008 0.849 0.142]\n",
      "Y [0.    0.    0.01  0.853 0.138]\n",
      "Y [0.    0.    0.011 0.828 0.161]\n",
      "Y [0.    0.    0.009 0.813 0.178]\n",
      "Y [0.    0.    0.013 0.768 0.219]\n",
      "Y [0.    0.    0.014 0.778 0.208]\n",
      "Y [0.    0.    0.01  0.795 0.194]\n",
      "Y [0.    0.    0.012 0.855 0.133]\n",
      "Y [0.    0.    0.037 0.676 0.288]\n",
      "dance [0.    0.    0.279 0.173 0.548]\n",
      "M [0.    0.    0.569 0.014 0.417]\n",
      "dance [0.001 0.    0.407 0.006 0.586]\n",
      "dance [0.    0.    0.117 0.003 0.88 ]\n",
      "dance [0.    0.    0.038 0.008 0.954]\n",
      "dance [0.    0.    0.028 0.016 0.955]\n",
      "dance [0.001 0.    0.026 0.015 0.958]\n",
      "dance [0.001 0.    0.042 0.02  0.937]\n",
      "dance [0.003 0.    0.045 0.026 0.926]\n",
      "dance [0.004 0.    0.095 0.029 0.872]\n",
      "dance [0.005 0.    0.162 0.041 0.792]\n",
      "dance [0.005 0.    0.182 0.068 0.745]\n",
      "dance [0.003 0.    0.225 0.104 0.668]\n",
      "dance [0.002 0.    0.287 0.109 0.602]\n",
      "dance [0.002 0.    0.169 0.138 0.691]\n",
      "dance [0.002 0.    0.138 0.166 0.694]\n",
      "dance [0.001 0.    0.107 0.133 0.759]\n",
      "dance [0.001 0.    0.083 0.103 0.813]\n",
      "dance [0.001 0.    0.063 0.1   0.837]\n",
      "dance [0.    0.    0.063 0.069 0.868]\n",
      "dance [0.    0.    0.082 0.047 0.871]\n",
      "dance [0.    0.    0.076 0.041 0.882]\n",
      "dance [0.    0.    0.079 0.021 0.9  ]\n",
      "dance [0.    0.    0.075 0.097 0.828]\n",
      "dance [0.    0.    0.05  0.088 0.862]\n",
      "dance [0.    0.    0.027 0.075 0.899]\n",
      "dance [0.    0.    0.012 0.035 0.953]\n",
      "dance [0.    0.    0.01  0.037 0.953]\n",
      "dance [0.    0.    0.007 0.043 0.95 ]\n",
      "dance [0.    0.    0.004 0.063 0.932]\n",
      "dance [0.    0.    0.003 0.062 0.936]\n",
      "dance [0.    0.    0.002 0.08  0.918]\n",
      "dance [0.    0.    0.001 0.097 0.901]\n",
      "dance [0.    0.    0.001 0.183 0.816]\n",
      "dance [0.    0.    0.001 0.165 0.834]\n",
      "dance [0.    0.    0.001 0.108 0.891]\n",
      "dance [0.    0.    0.001 0.104 0.896]\n",
      "dance [0.   0.   0.   0.12 0.88]\n",
      "dance [0.    0.    0.001 0.153 0.847]\n",
      "dance [0.    0.    0.    0.205 0.795]\n",
      "dance [0.    0.    0.    0.289 0.711]\n",
      "dance [0.    0.    0.    0.287 0.713]\n",
      "dance [0.    0.    0.    0.348 0.651]\n",
      "dance [0.    0.    0.    0.339 0.661]\n",
      "dance [0.    0.    0.    0.433 0.567]\n",
      "dance [0.    0.    0.    0.427 0.573]\n",
      "dance [0.    0.    0.    0.368 0.632]\n",
      "dance [0.    0.    0.    0.4   0.599]\n",
      "dance [0.  0.  0.  0.4 0.6]\n",
      "dance [0.    0.    0.    0.449 0.551]\n",
      "Y [0.    0.    0.001 0.521 0.479]\n",
      "Y [0.    0.    0.001 0.51  0.489]\n",
      "Y [0.    0.    0.    0.658 0.341]\n",
      "Y [0.    0.    0.    0.758 0.241]\n",
      "Y [0.    0.    0.001 0.793 0.206]\n",
      "Y [0.    0.    0.001 0.591 0.407]\n",
      "Y [0.    0.    0.001 0.712 0.286]\n",
      "dance [0.    0.    0.001 0.461 0.537]\n",
      "dance [0.    0.    0.001 0.31  0.689]\n",
      "dance [0.    0.    0.001 0.384 0.615]\n",
      "Y [0.    0.    0.009 0.874 0.117]\n",
      "Y [0.    0.    0.011 0.985 0.004]\n",
      "Y [0.    0.    0.002 0.998 0.   ]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.011 0.989 0.   ]\n",
      "M [0.    0.    0.924 0.075 0.001]\n",
      "M [0.    0.    0.995 0.004 0.   ]\n",
      "M [0.    0.    0.999 0.001 0.   ]\n",
      "M [0.    0.    0.999 0.001 0.   ]\n",
      "M [0.    0.    0.999 0.001 0.   ]\n",
      "M [0.    0.    0.999 0.001 0.   ]\n",
      "M [0.    0.    0.999 0.001 0.   ]\n",
      "M [0.    0.    0.962 0.037 0.   ]\n",
      "M [0.001 0.107 0.582 0.305 0.005]\n",
      "C [0.    0.898 0.043 0.057 0.002]\n",
      "C [0.    0.983 0.003 0.013 0.   ]\n",
      "C [0.    0.998 0.001 0.001 0.   ]\n",
      "C [0.    0.999 0.    0.    0.   ]\n",
      "C [0.001 0.999 0.    0.    0.   ]\n",
      "C [0.001 0.999 0.    0.    0.   ]\n",
      "C [0.    0.999 0.    0.    0.   ]\n",
      "C [0.    0.999 0.    0.    0.   ]\n",
      "C [0.    0.999 0.    0.001 0.   ]\n",
      "C [0.001 0.998 0.    0.001 0.   ]\n",
      "C [0.001 0.999 0.    0.001 0.   ]\n",
      "C [0.    0.999 0.    0.    0.   ]\n",
      "C [0.    0.999 0.    0.    0.   ]\n",
      "C [0.    0.999 0.    0.001 0.   ]\n",
      "C [0.    0.998 0.    0.001 0.   ]\n",
      "C [0.002 0.98  0.    0.018 0.   ]\n",
      "C [0.002 0.95  0.001 0.047 0.   ]\n",
      "C [0.01  0.856 0.01  0.124 0.   ]\n",
      "C [0.285 0.435 0.046 0.234 0.   ]\n",
      "A [0.835 0.01  0.067 0.089 0.   ]\n",
      "A [0.972 0.    0.027 0.001 0.   ]\n",
      "A [0.997 0.    0.003 0.    0.   ]\n",
      "A [0.994 0.    0.006 0.    0.   ]\n",
      "A [0.996 0.    0.004 0.    0.   ]\n",
      "A [0.989 0.    0.011 0.    0.   ]\n",
      "A [0.959 0.    0.041 0.    0.   ]\n",
      "A [0.894 0.    0.106 0.    0.   ]\n",
      "A [0.943 0.    0.057 0.    0.   ]\n",
      "A [0.911 0.    0.089 0.    0.   ]\n",
      "A [0.898 0.    0.102 0.    0.   ]\n",
      "A [0.868 0.    0.132 0.    0.   ]\n",
      "A [0.908 0.    0.092 0.    0.   ]\n",
      "M [0.167 0.    0.833 0.    0.   ]\n",
      "M [0.    0.    0.997 0.003 0.   ]\n",
      "M [0.    0.    0.986 0.013 0.001]\n",
      "dance [0.    0.    0.352 0.105 0.543]\n",
      "dance [0.    0.    0.011 0.352 0.637]\n",
      "dance [0.    0.    0.    0.021 0.979]\n",
      "dance [0.   0.   0.   0.03 0.97]\n",
      "Y [0.   0.   0.   0.66 0.34]\n",
      "dance [0.    0.    0.001 0.331 0.668]\n",
      "dance [0.    0.    0.001 0.062 0.937]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.002 0.011 0.987]\n",
      "dance [0.    0.    0.009 0.07  0.921]\n",
      "dance [0.    0.    0.005 0.236 0.758]\n",
      "dance [0.    0.    0.002 0.326 0.672]\n",
      "dance [0.    0.    0.003 0.189 0.808]\n",
      "dance [0.    0.    0.001 0.264 0.734]\n",
      "dance [0.    0.    0.002 0.224 0.775]\n",
      "dance [0.    0.    0.    0.225 0.774]\n",
      "dance [0.    0.    0.001 0.274 0.725]\n",
      "Y [0.    0.    0.002 0.523 0.475]\n",
      "dance [0.    0.    0.011 0.396 0.593]\n",
      "Y [0.   0.   0.   0.91 0.09]\n",
      "Y [0.   0.   0.   0.99 0.01]\n",
      "Y [0.    0.    0.    0.998 0.002]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.968 0.032]\n",
      "Y [0.    0.    0.    0.738 0.261]\n",
      "dance [0.    0.    0.002 0.103 0.895]\n",
      "dance [0.    0.    0.    0.029 0.97 ]\n",
      "dance [0.    0.    0.    0.013 0.987]\n",
      "dance [0.    0.    0.    0.109 0.891]\n",
      "dance [0.    0.    0.    0.322 0.678]\n",
      "Y [0.   0.   0.   0.51 0.49]\n",
      "Y [0.    0.    0.    0.843 0.157]\n",
      "Y [0.    0.    0.    0.955 0.045]\n",
      "Y [0.    0.    0.    0.893 0.107]\n",
      "Y [0.    0.    0.    0.617 0.383]\n",
      "Y [0.    0.    0.    0.791 0.209]\n",
      "Y [0.    0.    0.    0.866 0.134]\n",
      "Y [0.    0.    0.    0.895 0.105]\n",
      "Y [0.    0.    0.    0.986 0.014]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.997 0.003]\n",
      "Y [0.    0.    0.    0.996 0.004]\n",
      "Y [0.    0.    0.    0.997 0.003]\n",
      "Y [0.    0.    0.    0.975 0.025]\n",
      "Y [0.    0.    0.    0.741 0.259]\n",
      "dance [0.    0.    0.    0.071 0.929]\n",
      "dance [0.    0.    0.    0.009 0.991]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0.    0.    0.    0.003 0.997]\n",
      "dance [0.    0.    0.    0.003 0.997]\n",
      "dance [0.    0.    0.    0.006 0.994]\n",
      "dance [0.    0.    0.    0.017 0.983]\n",
      "dance [0.    0.    0.    0.244 0.756]\n",
      "dance [0.    0.    0.    0.429 0.571]\n",
      "Y [0.    0.    0.    0.993 0.007]\n",
      "Y [0.    0.    0.    0.993 0.007]\n",
      "Y [0.    0.    0.    0.998 0.002]\n",
      "Y [0.    0.    0.    0.939 0.061]\n",
      "Y [0.    0.    0.163 0.823 0.014]\n",
      "M [0.    0.    0.998 0.001 0.   ]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0. 0. 1. 0. 0.]\n",
      "M [0.    0.    0.903 0.075 0.022]\n",
      "Y [0.    0.    0.    0.907 0.093]\n",
      "Y [0.    0.    0.    0.827 0.173]\n",
      "Y [0.    0.    0.    0.899 0.101]\n",
      "Y [0.    0.    0.    0.875 0.125]\n",
      "Y [0.    0.    0.    0.643 0.357]\n",
      "Y [0.    0.    0.    0.563 0.437]\n",
      "Y [0.   0.   0.   0.67 0.33]\n",
      "Y [0.    0.    0.    0.931 0.069]\n",
      "Y [0.    0.    0.    0.919 0.081]\n",
      "Y [0.    0.    0.    0.905 0.095]\n",
      "Y [0.   0.   0.   0.76 0.24]\n",
      "dance [0.    0.    0.    0.414 0.586]\n",
      "dance [0.    0.    0.    0.237 0.763]\n",
      "dance [0.    0.    0.    0.283 0.717]\n",
      "dance [0.    0.    0.    0.191 0.809]\n",
      "dance [0.    0.    0.    0.043 0.957]\n",
      "dance [0.    0.    0.    0.012 0.988]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.002 0.998]\n",
      "dance [0.    0.    0.    0.006 0.994]\n",
      "dance [0.    0.    0.    0.053 0.947]\n",
      "dance [0.    0.    0.    0.017 0.983]\n",
      "dance [0.    0.    0.    0.004 0.996]\n",
      "dance [0.    0.    0.    0.002 0.998]\n",
      "dance [0.    0.    0.    0.002 0.998]\n",
      "dance [0.   0.   0.   0.04 0.96]\n",
      "dance [0.    0.    0.    0.075 0.925]\n",
      "dance [0.    0.    0.    0.157 0.843]\n",
      "dance [0.    0.    0.    0.407 0.593]\n",
      "Y [0.    0.    0.    0.654 0.346]\n",
      "Y [0.    0.    0.    0.965 0.035]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.    0.957 0.043]\n",
      "dance [0.    0.    0.    0.093 0.907]\n",
      "dance [0.    0.    0.    0.005 0.995]\n",
      "dance [0.    0.    0.    0.002 0.998]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0.    0.    0.    0.024 0.976]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0.    0.    0.014 0.    0.986]\n",
      "dance [0.    0.    0.011 0.    0.989]\n",
      "dance [0.    0.    0.005 0.    0.995]\n",
      "dance [0.    0.    0.001 0.    0.998]\n",
      "dance [0.    0.    0.    0.005 0.995]\n",
      "dance [0.   0.   0.   0.03 0.97]\n",
      "dance [0.   0.   0.   0.04 0.96]\n",
      "dance [0.    0.    0.    0.058 0.942]\n",
      "dance [0.    0.    0.    0.071 0.929]\n",
      "dance [0.    0.    0.    0.084 0.916]\n",
      "dance [0.    0.    0.    0.067 0.933]\n",
      "dance [0.    0.    0.    0.107 0.893]\n",
      "dance [0.    0.    0.    0.083 0.917]\n",
      "dance [0.    0.    0.    0.098 0.902]\n",
      "dance [0.   0.   0.   0.27 0.73]\n",
      "dance [0.    0.    0.    0.109 0.891]\n",
      "dance [0.    0.    0.    0.031 0.969]\n",
      "dance [0.    0.    0.    0.036 0.964]\n",
      "dance [0.    0.    0.    0.051 0.949]\n",
      "dance [0.    0.    0.    0.035 0.965]\n",
      "dance [0.    0.    0.    0.045 0.955]\n",
      "dance [0.    0.    0.    0.271 0.729]\n",
      "dance [0.    0.    0.    0.253 0.747]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0. 0. 0. 1. 0.]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.999 0.001]\n",
      "Y [0.    0.    0.    0.985 0.015]\n",
      "Y [0.    0.    0.    0.977 0.023]\n",
      "Y [0.    0.    0.    0.861 0.139]\n",
      "Y [0.    0.    0.    0.868 0.132]\n",
      "dance [0.    0.    0.    0.312 0.688]\n",
      "dance [0.    0.    0.    0.111 0.889]\n",
      "dance [0.    0.    0.    0.215 0.785]\n",
      "dance [0.    0.    0.    0.158 0.842]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.061 0.939]\n",
      "Y [0.    0.    0.    0.778 0.222]\n",
      "dance [0.    0.    0.    0.338 0.662]\n",
      "Y [0.    0.    0.    0.603 0.397]\n",
      "Y [0.  0.  0.  0.9 0.1]\n",
      "Y [0.   0.   0.   0.65 0.35]\n",
      "dance [0.    0.    0.    0.138 0.862]\n",
      "dance [0.    0.    0.    0.092 0.908]\n",
      "dance [0.   0.   0.   0.01 0.99]\n",
      "Y [0.    0.    0.    0.782 0.218]\n",
      "dance [0.    0.    0.001 0.114 0.885]\n",
      "Y [0.    0.    0.004 0.996 0.   ]\n",
      "Y [0.    0.    0.036 0.483 0.481]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0. 0. 0. 0. 1.]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.001 0.999]\n",
      "dance [0.    0.    0.    0.004 0.996]\n",
      "dance [0.    0.    0.    0.008 0.992]\n",
      "dance [0.    0.    0.    0.057 0.943]\n",
      "dance [0.    0.    0.    0.389 0.611]\n",
      "Y [0.   0.   0.   0.58 0.42]\n",
      "Y [0.    0.    0.    0.616 0.384]\n",
      "Y [0.    0.    0.    0.542 0.458]\n",
      "dance [0.    0.    0.    0.477 0.523]\n",
      "dance [0.    0.    0.    0.438 0.562]\n",
      "dance [0.    0.    0.    0.385 0.615]\n",
      "dance [0.    0.    0.    0.368 0.632]\n",
      "dance [0.    0.    0.    0.354 0.646]\n",
      "dance [0.    0.    0.    0.383 0.617]\n",
      "dance [0.    0.    0.    0.307 0.693]\n",
      "dance [0.    0.    0.    0.339 0.661]\n",
      "dance [0.    0.    0.    0.403 0.597]\n",
      "dance [0.    0.    0.    0.421 0.579]\n",
      "dance [0.    0.    0.    0.433 0.567]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n",
      "2021-12-17 11:14:56.779 Python[3368:402358] ApplePersistenceIgnoreState: Existing state will not be touched. New state will be written to (null)\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "python 03_pose_predictions.py"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}