WORD2Vec.ipynb

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  "metadata": {
    "colab": {
      "name": "WORD2Vec",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPwqNWrmnto850Yaee6MoiN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/seemasafar/nlp-in-python/blob/master/WORD2Vec.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "pUiGP5XbW-PJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from collections import defaultdict\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "## Randomly initialise\n",
        "getW1 = [[0.236, -0.962, 0.686, 0.785, -0.454, -0.833, -0.744, 0.677, -0.427, -0.066],\n",
        "\t\t[-0.907, 0.894, 0.225, 0.673, -0.579, -0.428, 0.685, 0.973, -0.070, -0.811],\n",
        "\t\t[-0.576, 0.658, -0.582, -0.112, 0.662, 0.051, -0.401, -0.921, -0.158, 0.529],\n",
        "\t\t[0.517, 0.436, 0.092, -0.835, -0.444, -0.905, 0.879, 0.303, 0.332, -0.275],\n",
        "\t\t[0.859, -0.890, 0.651, 0.185, -0.511, -0.456, 0.377, -0.274, 0.182, -0.237],\n",
        "\t\t[0.368, -0.867, -0.301, -0.222, 0.630, 0.808, 0.088, -0.902, -0.450, -0.408],\n",
        "\t\t[0.728, 0.277, 0.439, 0.138, -0.943, -0.409, 0.687, -0.215, -0.807, 0.612],\n",
        "\t\t[0.593, -0.699, 0.020, 0.142, -0.638, -0.633, 0.344, 0.868, 0.913, 0.429],\n",
        "\t\t[0.447, -0.810, -0.061, -0.495, 0.794, -0.064, -0.817, -0.408, -0.286, 0.149]]\n",
        "\n",
        "getW2 = [[-0.868, -0.406, -0.288, -0.016, -0.560, 0.179, 0.099, 0.438, -0.551],\n",
        "\t\t[-0.395, 0.890, 0.685, -0.329, 0.218, -0.852, -0.919, 0.665, 0.968],\n",
        "\t\t[-0.128, 0.685, -0.828, 0.709, -0.420, 0.057, -0.212, 0.728, -0.690],\n",
        "\t\t[0.881, 0.238, 0.018, 0.622, 0.936, -0.442, 0.936, 0.586, -0.020],\n",
        "\t\t[-0.478, 0.240, 0.820, -0.731, 0.260, -0.989, -0.626, 0.796, -0.599],\n",
        "\t\t[0.679, 0.721, -0.111, 0.083, -0.738, 0.227, 0.560, 0.929, 0.017],\n",
        "\t\t[-0.690, 0.907, 0.464, -0.022, -0.005, -0.004, -0.425, 0.299, 0.757],\n",
        "\t\t[-0.054, 0.397, -0.017, -0.563, -0.551, 0.465, -0.596, -0.413, -0.395],\n",
        "\t\t[-0.838, 0.053, -0.160, -0.164, -0.671, 0.140, -0.149, 0.708, 0.425],\n",
        "\t\t[0.096, -0.995, -0.313, 0.881, -0.402, -0.631, -0.660, 0.184, 0.487]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8t-n415XGV-",
        "colab_type": "code",
        "outputId": "413f9c2a-f937-4610-e40c-757929d85bc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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      },
      "source": [
        "class word2vec():\n",
        "\n",
        "\tdef __init__(self):\n",
        "\t\tself.n = settings['n']\n",
        "\t\tself.lr = settings['learning_rate']\n",
        "\t\tself.epochs = settings['epochs']\n",
        "\t\tself.window = settings['window_size']\n",
        "\n",
        "\tdef generate_training_data(self, settings, corpus):\n",
        "\t\tprint('Find unique word counts using dictonary')\n",
        "\t\tword_counts = defaultdict(int)\n",
        "\t\tfor row in corpus:\n",
        "\t\t\tfor word in row:\n",
        "\t\t\t\tword_counts[word] += 1\n",
        "\t\t\n",
        "\t\tprint(\"THE WORD COUNTS ARE\",word_counts)\t\t\n",
        "  ###########################################\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
        "\t\t\t\n",
        "\t\t\n",
        "\t\t## How many unique words in vocab? 9\n",
        "\t\tself.v_count = len(word_counts.keys())\n",
        "\t\t#########################\n",
        "\t\t# print(self.v_count)\t#\n",
        "\t\t# 9\t\t\t\t\t\t#\n",
        "\t\t#########################\n",
        "\n",
        "\t\t# Generate Lookup Dictionaries (vocab)\n",
        "\t\tself.words_list = list(word_counts.keys())\n",
        "\t\t#################################################################################################\n",
        "\t\tprint(\" trying to print self.words_list\",self.words_list)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
        "\t\t# trying to print self.words_list ['i', 'will', 'love', 'the', 'light', 'for', 'it', 'shows', 'me']\t#\n",
        "\t\t#################################################################################################\n",
        "\t\t\n",
        "\t\t# Generate word:index\n",
        "\t\tself.word_index = dict((word, i) for i, word in enumerate(self.words_list))\n",
        "\t\t#############################################################################################################################\n",
        "\t\tprint(\"The index of the corpus index are\",self.word_index)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n",
        "\t\t# # The index of the corpus index are {'i': 0, 'will': 1, 'love': 2, 'the': 3, 'light': 4, 'for': 5, 'it': 6, 'shows': 7, 'me': 8}\t#\n",
        "\t\t#############################################################################################################################\n",
        "\n",
        "\t\t# Generate index:word\n",
        "\t\tself.index_word = dict((i, word) for i, word in enumerate(self.words_list))\n",
        "\t\t#############################################################################################################################\n",
        "\t\tprint(\"The index of words are \" ,self.index_word)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n",
        "\t\t# The index of words are  {0: 'i', 1: 'will', 2: 'love', 3: 'the', 4: 'light', 5: 'for', 6: 'it', 7: 'shows', 8: 'me'}\t#\n",
        "\t\t#############################################################################################################################\n",
        "\n",
        "\t\ttraining_data = []\n",
        "\n",
        "\t\t# Cycle through each sentence in corpus\n",
        "\t\tfor sentence in corpus:\n",
        "\t\t\tsent_len = len(sentence)\n",
        "\n",
        "\t\tprint(\"The length of the sentence is\",sent_len)\n",
        "\n",
        "\t\t\t# Cycle through each word in sentence\n",
        "\t\tfor i, word in enumerate(sentence):\n",
        "\t\t\t\t# Convert target word to one-hot\n",
        "\t\t\t\tw_target = self.word2onehot(sentence[i])\n",
        "\n",
        "\t\t\t\t# Cycle through context window\n",
        "\t\t\t\tw_context = []\n",
        "\n",
        "\t\t\t\t# Note: window_size 2 will have range of 5 values\n",
        "\t\t\t\tfor j in range(i - self.window, i + self.window+1):\n",
        "\t\t\t\t\t# Criteria for context word \n",
        "\t\t\t\t\t# 1. Target word cannot be context word (j != i)\n",
        "\t\t\t\t\t# 2. Index must be greater or equal than 0 (j >= 0) - if not list index out of range\n",
        "\t\t\t\t\t# 3. Index must be less or equal than length of sentence (j <= sent_len-1) - if not list index out of range \n",
        "\t\t\t\t\tif j != i and j <= sent_len-1 and j >= 0:\n",
        "\t\t\t\t\t\t# Append the one-hot representation of word to w_context\n",
        "\t\t\t\t\t\tw_context.append(self.word2onehot(sentence[j]))\n",
        "\t\t\t\t\t\tprint(\"Trying to append\", sentence[i], sentence[j]) \n",
        "\t\t\t\t\t\t#########################\n",
        "\t\t\t\t\t\t# Example:\t\t\t\t#\n",
        "\t\t\t\t\t\t#Trying to append i will\n",
        "\t\t\t\t\t\t#Trying to append i love\n",
        "\t\t\t\t\t\n",
        "\t\t\t\t\t\t#########################\n",
        "\t\t\t\t\t\t\n",
        "\t\t\t\t# training_data contains a one-hot representation of the target word and context words\n",
        "\t\t\t\t#################################################################################################\n",
        "\t\t\t\t# Example:\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n",
        "\t\t\t\t# [Target] i, [Context] will, [Context] love\t\t\t\t\t\t\t\t\t#\n",
        "\t\t\t\t\tprint(\"The training data is \", training_data)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n",
        "\t\t\t\t# [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]]]\t#\n",
        "\t\t\t\t#################################################################################################\n",
        "\t\t\t\ttraining_data.append([w_target, w_context])\n",
        "\n",
        "\t\treturn np.array(training_data)\n",
        "\n",
        "\tdef word2onehot(self, word):\n",
        "\t\t# word_vec - initialise a blank vector\n",
        "\t\tword_vec = [0 for i in range(0, self.v_count)] # Alternative - np.zeros(self.v_count)\n",
        "\t\t#############################\n",
        "\t#\tprint(\"The output of word_vec\")\n",
        "\t#\tprint(word_vec)\n",
        "\t\t# [0, 0, 0, 0, 0, 0, 0, 0]\t#\n",
        "\t\t#############################\n",
        "\n",
        "\t\t# Get ID of word from word_index\n",
        "\t\tword_index = self.word_index[word]\n",
        "\n",
        "\t\t# Change value from 0 to 1 according to ID of the word\n",
        "\t\tword_vec[word_index] = 1\n",
        "\n",
        "\t\treturn word_vec\n",
        "\n",
        "\tdef train(self, training_data):\n",
        "\t\t# Initialising weight matrices\n",
        "\t\t# np.random.uniform(HIGH, LOW, OUTPUT_SHAPE)\n",
        "\t\t# https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.uniform.html\n",
        "\t\tself.w1 = np.array(getW1)\n",
        "\t\tself.w2 = np.array(getW2)\n",
        "\t\t# self.w1 = np.random.uniform(-1, 1, (self.v_count, self.n))\n",
        "\t\t# self.w2 = np.random.uniform(-1, 1, (self.n, self.v_count))\n",
        "\t\t\n",
        "\t\t# Cycle through each epoch\n",
        "\t\tfor i in range(self.epochs):\n",
        "\t\t\t# Intialise loss to 0\n",
        "\t\t\tself.loss = 0\n",
        "\t\t\t# Cycle through each training sample\n",
        "\t\t\t# w_t = vector for target word, w_c = vectors for context words\n",
        "\t\t\tfor w_t, w_c in training_data:\n",
        "\t\t\t\t# Forward pass\n",
        "\t\t\t\t# 1. predicted y using softmax (y_pred) 2. matrix of hidden layer (h) 3. output layer before softmax (u)\n",
        "\t\t\t\ty_pred, h, u = self.forward_pass(w_t)\n",
        "\t\t\t\t#########################################\n",
        "\t\t\t\t# print(\"Vector for target word:\", w_t)\t#\n",
        "\t\t\t\t# print(\"W1-before backprop\", self.w1)\t#\n",
        "\t\t\t\t# print(\"W2-before backprop\", self.w2)\t#\n",
        "\t\t\t\t#########################################\n",
        "\n",
        "\t\t\t\t# Calculate error\n",
        "\t\t\t\t# 1. For a target word, calculate difference between y_pred and each of the context words\n",
        "\t\t\t\t# 2. Sum up the differences using np.sum to give us the error for this particular target word\n",
        "\t\t\t\tEI = np.sum([np.subtract(y_pred, word) for word in w_c], axis=0)\n",
        "\t\t\t\t#########################\n",
        "\t\t\t\t# print(\"Error\", EI)\t#\n",
        "\t\t\t\t#########################\n",
        "\n",
        "\t\t\t\t# Backpropagation\n",
        "\t\t\t\t# We use SGD to backpropagate errors - calculate loss on the output layer \n",
        "\t\t\t\tself.backprop(EI, h, w_t)\n",
        "\t\t\t\t#########################################\n",
        "\t\t\t\t#print(\"W1-after backprop\", self.w1)\t#\n",
        "\t\t\t\t#print(\"W2-after backprop\", self.w2)\t#\n",
        "\t\t\t\t#########################################\n",
        "\n",
        "\t\t\t\t# Calculate loss\n",
        "\t\t\t\t# There are 2 parts to the loss function\n",
        "\t\t\t\t# Part 1: -ve sum of all the output +\n",
        "\t\t\t\t# Part 2: length of context words * log of sum for all elements (exponential-ed) in the output layer before softmax (u)\n",
        "\t\t\t\t# Note: word.index(1) returns the index in the context word vector with value 1\n",
        "\t\t\t\t# Note: u[word.index(1)] returns the value of the output layer before softmax\n",
        "\t\t\t\tself.loss += -np.sum([u[word.index(1)] for word in w_c]) + len(w_c) * np.log(np.sum(np.exp(u)))\n",
        "\t\t\t #\tplt.plot(self.loss)\n",
        "\t\t\t\t#plt.show()\n",
        "\t\t\t\t#############################################################\n",
        "\t\t\t\t# Break if you want to see weights after first target word \t#\n",
        "\t\t\t\t# break \t\t\t\t\t\t\t\t\t\t\t\t\t#\n",
        "\t\t\t\t#############################################################\n",
        "\t\t\tprint('Epoch:', i, \"Loss:\", self.loss)\n",
        "\n",
        "\tdef forward_pass(self, x):\n",
        "\t\t# x is one-hot vector for target word, shape - 9x1\n",
        "\t\t# Run through first matrix (w1) to get hidden layer - 10x9 dot 9x1 gives us 10x1\n",
        "\t\th = np.dot(x, self.w1)\n",
        "\t\t# Dot product hidden layer with second matrix (w2) - 9x10 dot 10x1 gives us 9x1\n",
        "\t\tu = np.dot(h, self.w2)\n",
        "\t\t# Run 1x9 through softmax to force each element to range of [0, 1] - 1x8\n",
        "\t\ty_c = self.softmax(u)\n",
        "\t\treturn y_c, h, u\n",
        "\n",
        "\tdef softmax(self, x):\n",
        "\t\te_x = np.exp(x - np.max(x))\n",
        "\t\treturn e_x / e_x.sum(axis=0)\n",
        "\n",
        "\tdef backprop(self, e, h, x):\n",
        "\t\t# https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.outer.html\n",
        "\t\t# Column vector EI represents row-wise sum of prediction errors across each context word for the current center word\n",
        "\t\t# Going backwards, we need to take derivative of E with respect of w2\n",
        "\t\t# h - shape 10x1, e - shape 9x1, dl_dw2 - shape 10x9\n",
        "\t\t# x - shape 9x1, w2 - 10x9, e.T - 9x1\n",
        "\t\tdl_dw2 = np.outer(h, e)\n",
        "\t\tdl_dw1 = np.outer(x, np.dot(self.w2, e.T))\n",
        "\t\t########################################\n",
        "\t\t# print('Delta for w2', dl_dw2)\t\t\t#\n",
        "\t\t# print('Hidden layer', h)\t\t\t\t#\n",
        "\t\t# print('np.dot', np.dot(self.w2, e.T))\t#\n",
        "\t\t# print('Delta for w1', dl_dw1)\t\t\t#\n",
        "\t\t#########################################\n",
        "\n",
        "\t\t# Update weights\n",
        "\t\tself.w1 = self.w1 - (self.lr * dl_dw1)\n",
        "\t\tself.w2 = self.w2 - (self.lr * dl_dw2)\n",
        "\n",
        "\t# Get vector from word\n",
        "\tdef word_vec(self, word):\n",
        "\t\tw_index = self.word_index[word]\n",
        "\t\tv_w = self.w1[w_index]\n",
        "\t\treturn v_w\n",
        "\n",
        "\t# Input vector, returns nearest word(s)\n",
        "\tdef vec_sim(self, word, top_n):\n",
        "\t\tv_w1 = self.word_vec(word)\n",
        "\t\tword_sim = {}\n",
        "\n",
        "\t\tfor i in range(self.v_count):\n",
        "\t\t\t# Find the similary score for each word in vocab\n",
        "\t\t\tv_w2 = self.w1[i]\n",
        "\t\t\ttheta_sum = np.dot(v_w1, v_w2)\n",
        "\t\t\ttheta_den = np.linalg.norm(v_w1) * np.linalg.norm(v_w2)\n",
        "\t\t\ttheta = theta_sum / theta_den\n",
        "\t\t\tplt.plot(v_w1,v_w2)\n",
        "\t\t\tplt.show()\n",
        "\t\t\tword = self.index_word[i]\n",
        "\t\t\tword_sim[word] = theta\n",
        "\t\t\t\n",
        "\n",
        "\t\twords_sorted = sorted(word_sim.items(), key=lambda kv: kv[1], reverse=True)\n",
        "\n",
        "\t\tfor word, sim in words_sorted[:top_n]:\n",
        "\t\t\tprint(word, sim)\n",
        "\n",
        "#####################################################################\n",
        "settings = {\n",
        "\t'window_size': 2,\t\t\t# context window +- center word\n",
        "\t'n': 10,\t\t\t\t\t# dimensions of word embeddings, also refer to size of hidden layer\n",
        "\t'epochs': 50,\t\t\t\t# number of training epochs\n",
        "\t'learning_rate': 0.01\t\t# learning rate\n",
        "}\n",
        "\n",
        "text = \"The real opportunity for success lies within the person but\"\n",
        "\n",
        "\n",
        "# Note the .lower() as upper and lowercase does not matter in our implementation\n",
        "# [['natural', 'language', 'processing', 'and', 'machine', 'learning', 'is', 'fun', 'and', 'exciting']]\n",
        "corpus = [[word.lower() for word in text.split()]]\n",
        "\n",
        "# Initialise object\n",
        "w2v = word2vec()\n",
        "\n",
        "# Numpy ndarray with one-hot representation for [target_word, context_words]\n",
        "training_data = w2v.generate_training_data(settings, corpus)\n",
        "#pl= np.array(training_data)\n",
        "#plt.plot(pl)\n",
        "#plt.show()\n",
        "# Training\n",
        "w2v.train(training_data)\n",
        "\n",
        "# Get vector for word\n",
        "word = \"person\"\n",
        "vec = w2v.word_vec(word)\n",
        "print(\"The vector value of\" , word)\n",
        "print(\"is\" , vec)\n",
        "##print(word, vec)\n",
        "\n",
        "# Find similar words\n",
        "print(\"Three similar words of\",word)\n",
        "print(\"is\")\n",
        "w2v.vec_sim(\"person\", 3)\n",
        "\n",
        "\n",
        "    \n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Find unique word counts using dictonary\n",
            "THE WORD COUNTS ARE defaultdict(<class 'int'>, {'the': 2, 'real': 1, 'opportunity': 1, 'for': 1, 'success': 1, 'lies': 1, 'within': 1, 'person': 1, 'but': 1})\n",
            " trying to print self.words_list ['the', 'real', 'opportunity', 'for', 'success', 'lies', 'within', 'person', 'but']\n",
            "The index of the corpus index are {'the': 0, 'real': 1, 'opportunity': 2, 'for': 3, 'success': 4, 'lies': 5, 'within': 6, 'person': 7, 'but': 8}\n",
            "The index of words are  {0: 'the', 1: 'real', 2: 'opportunity', 3: 'for', 4: 'success', 5: 'lies', 6: 'within', 7: 'person', 8: 'but'}\n",
            "The length of the sentence is 10\n",
            "The training data is  []\n",
            "The training data is  []\n",
            "The training data is  []\n",
            "Trying to append the real\n",
            "The training data is  []\n",
            "Trying to append the opportunity\n",
            "The training data is  []\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append real the\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append real opportunity\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append real for\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append opportunity the\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append opportunity real\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append opportunity for\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append opportunity success\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append for real\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]]]\n",
            "Trying to append for opportunity\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]]]\n",
            "Trying to append for success\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]]]\n",
            "Trying to append for lies\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]]]\n",
            "Trying to append success opportunity\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]]]\n",
            "Trying to append success for\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]]]\n",
            "Trying to append success lies\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]]]\n",
            "Trying to append success within\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]]]\n",
            "Trying to append lies for\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]]]\n",
            "Trying to append lies success\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]]]\n",
            "Trying to append lies within\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]]]\n",
            "Trying to append lies the\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]]]\n",
            "Trying to append within success\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append within lies\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append within the\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append within person\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]]]\n",
            "Trying to append the lies\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]]]\n",
            "Trying to append the within\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]]]\n",
            "Trying to append the person\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]]]\n",
            "Trying to append the but\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]]]\n",
            "Trying to append person within\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]]]\n",
            "Trying to append person the\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]]]\n",
            "Trying to append person but\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]]]\n",
            "Trying to append but the\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]], [[0, 0, 0, 0, 0, 0, 0, 1, 0], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]]]\n",
            "Trying to append but person\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]], [[0, 0, 0, 0, 0, 0, 0, 1, 0], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]], [[0, 0, 0, 0, 0, 0, 0, 1, 0], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]], [[0, 0, 0, 0, 0, 0, 0, 1, 0], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]]]\n",
            "The training data is  [[[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]]], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]]], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0]]], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0]]], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [[0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0]]], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0]]], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0]]], [[1, 0, 0, 0, 0, 0, 0, 0, 0], [[0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]], [[0, 0, 0, 0, 0, 0, 0, 1, 0], [[0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]]]]\n",
            "Epoch: 0 Loss: 88.85761392159785\n",
            "Epoch: 1 Loss: 86.51876307315091\n",
            "Epoch: 2 Loss: 84.42743395431643\n",
            "Epoch: 3 Loss: 82.54779198622698\n",
            "Epoch: 4 Loss: 80.85085346657907\n",
            "Epoch: 5 Loss: 79.31286354574956\n",
            "Epoch: 6 Loss: 77.9140892882165\n",
            "Epoch: 7 Loss: 76.63792119127208\n",
            "Epoch: 8 Loss: 75.47020503256482\n",
            "Epoch: 9 Loss: 74.3987452620163\n",
            "Epoch: 10 Loss: 73.41293530684203\n",
            "Epoch: 11 Loss: 72.5034809363001\n",
            "Epoch: 12 Loss: 71.66219119399476\n",
            "Epoch: 13 Loss: 70.88181792911035\n",
            "Epoch: 14 Loss: 70.15593003034229\n",
            "Epoch: 15 Loss: 69.4788123676266\n",
            "Epoch: 16 Loss: 68.8453823965541\n",
            "Epoch: 17 Loss: 68.25111956202\n",
            "Epoch: 18 Loss: 67.69200420993175\n",
            "Epoch: 19 Loss: 67.1644638165028\n",
            "Epoch: 20 Loss: 66.66532509119735\n",
            "Epoch: 21 Loss: 66.19177099860848\n",
            "Epoch: 22 Loss: 65.74130205338942\n",
            "Epoch: 23 Loss: 65.31170142955767\n",
            "Epoch: 24 Loss: 64.90100353412744\n",
            "Epoch: 25 Loss: 64.5074657554159\n",
            "Epoch: 26 Loss: 64.12954312896056\n",
            "Epoch: 27 Loss: 63.765865681933455\n",
            "Epoch: 28 Loss: 63.41521822825726\n",
            "Epoch: 29 Loss: 63.07652239584837\n",
            "Epoch: 30 Loss: 62.74882067683085\n",
            "Epoch: 31 Loss: 62.431262302108536\n",
            "Epoch: 32 Loss: 62.123090753507746\n",
            "Epoch: 33 Loss: 61.82363273957177\n",
            "Epoch: 34 Loss: 61.5322884746019\n",
            "Epoch: 35 Loss: 61.248523114280275\n",
            "Epoch: 36 Loss: 60.97185921479582\n",
            "Epoch: 37 Loss: 60.70187009552629\n",
            "Epoch: 38 Loss: 60.43817399778338\n",
            "Epoch: 39 Loss: 60.18042894375669\n",
            "Epoch: 40 Loss: 59.928328210513854\n",
            "Epoch: 41 Loss: 59.68159634369726\n",
            "Epoch: 42 Loss: 59.43998564440518\n",
            "Epoch: 43 Loss: 59.20327307069607\n",
            "Epoch: 44 Loss: 58.97125750225584\n",
            "Epoch: 45 Loss: 58.743757323085084\n",
            "Epoch: 46 Loss: 58.520608282662394\n",
            "Epoch: 47 Loss: 58.30166160099072\n",
            "Epoch: 48 Loss: 58.08678228730163\n",
            "Epoch: 49 Loss: 57.87584764604165\n",
            "The vector value of person\n",
            "is [ 0.21439261 -0.80437497 -0.35025728  0.63402338 -0.79102427 -0.42240599\n",
            "  0.0225595   0.72663437  0.67648025  0.68259091]\n",
            "Three similar words of person\n",
            "is\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3Z28VlVYOny6sXkuoKkU9XVBThhrW2Z+osCAmDQutblbrex5lqJ8nneKeN7cR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\nZcTyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6mrJ01tX/8NtSdmkt25ueGA6eLUGtFThcKFsN1ZWtGkIOOr2w+uJm8ecrdOWg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\nGJiZGS4GZmYG/Ady1ug69AEuLQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZE5dEk5pbwoIJfRns59Eg/UtIL2DMwkjc7S355gZzHErjKyirZHdCJtviMtid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\nH+TrmDN1PpeXiw0v3OtH5AuD+XhcL5x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iNu076nekQEvrGM/U67P517RR/H58Lt07xPPE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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AV7seilhRLs/a/cXYWJoxbYBxTbjQS6IQQlwphDguhMgUQjzTyePWQojPdY/v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\n304AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/k/uw89lMXxqAhZkJq+f43fSXKSEjl0W7Ihjn3pGRTajEtjG4Va3/HR52ZbX+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\nMnZYH0kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AWC1PPVvzjtZaK6XqvLiFUiocmAOM0lobzQ8/gynBuADTMK1GXqhtf631NPM2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\nQGGMMaYSBwpjjDGV/AZQv/QDoflKrgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TgmTKipPZWGCZ9M6y2Khw3rsqk/euyqSt187Oo008e2S0hqEkM567StK5qySd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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VQSqYGTf+R8+6eGoRkZyV7A3kacDIyPZI4PkEcz4EBppZAzNrSPjmccLLRCIi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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m9m/6on0uNm14dngwjw1qz9oDmSyITWH2qoO8bd+2fgc+6zbq191YaTQSU1Pj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\nwhhjTJGsUBhjjCmSFQpjjDFF+n+Fh/37un8omgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "person 0.9999999999999999\n",
            "the 0.4843687952135665\n",
            "success 0.4080258911381475\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbPDFG9SxvUZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "2ae422ff-2b7d-4334-9a79-a95cc17d5edd"
      },
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "data = np.array([\n",
        "    [1, 2],\n",
        "    [2, 3],\n",
        "    [3, 6],\n",
        "])\n",
        "x, y = data.T\n",
        "plt.scatter(x,y)\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IwScI0pv-aPQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#TRYING TO PLOT \n",
        "rom gensim.models import Word2Vec\n",
        "from sklearn.decomposition import PCA\n",
        "from matplotlib import pyplot\n",
        "# define training data\n",
        "sentences = [['this', 'is', 'the', 'first', 'sentence', 'for', 'word2vec'],\n",
        "            ['this', 'is', 'the', 'second', 'sentence'],\n",
        "            ['yet', 'another', 'sentence'],\n",
        "            ['one', 'more', 'sentence'],\n",
        "            ['and', 'the', 'final', 'sentence']]\n",
        "# train model\n",
        "model_1 = Word2Vec(sentences, size=300, min_count=1)\n",
        "\n",
        "# fit a 2d PCA model to the vectors\n",
        "X = model_1[model_1.wv.vocab]\n",
        "pca = PCA(n_components=2)\n",
        "result = pca.fit_transform(X)\n",
        "# create a scatter plot of the projection\n",
        "pyplot.scatter(result[:, 0], result[:, 1])\n",
        "words = list(model_1.wv.vocab)\n",
        "for i, word in enumerate(words):\n",
        "    pyplot.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
        "pyplot.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RBDBiEAZHAI",
        "colab_type": "code",
        "outputId": "5634a69c-cb16-4af9-81d4-3e119055d5d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "from gensim.models import Word2Vec\n",
        "from sklearn.decomposition import PCA\n",
        "from matplotlib import pyplot\n",
        "# define training data\n",
        "sentences = [['this', 'is', 'the', 'first', 'sentence', 'for', 'word2vec'],\n",
        "            ['this', 'is', 'the', 'second', 'sentence'],\n",
        "            ['yet', 'another', 'sentence'],\n",
        "            ['one', 'more', 'sentence'],\n",
        "            ['and', 'the', 'final', 'sentence']]\n",
        "# train model\n",
        "model_1 = Word2Vec(sentences, size=300, min_count=1)\n",
        "\n",
        "# fit a 2d PCA model to the vectors\n",
        "X = model_1[model_1.wv.vocab]\n",
        "pca = PCA(n_components=2)\n",
        "result = pca.fit_transform(X)\n",
        "# create a scatter plot of the projection\n",
        "pyplot.scatter(result[:, 0], result[:, 1])\n",
        "words = list(model_1.wv.vocab)\n",
        "for i, word in enumerate(words):\n",
        "    pyplot.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
        "pyplot.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V00TkGmCZiOxU1dXuFVRVRcTjbJCqOgWYAq7JFMs+XGOMqRgKmy3Zv91vSEycRVhYGFdc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\nr1gCMcYY4xVLIMYYY7xiCcQYY4xX/h/9Uda0wjWPeQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}