dl01.ipynb

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      "name": "dl.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ygw365/dl/blob/master/dl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "JCoUeO2TcxW2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cbuUZquieQvs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = tf.constant(np.random.rand(32).astype(np.float32))\n",
        "y = tf.constant ([1,2,3])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QlxbugXueQ51",
        "colab_type": "code",
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        "outputId": "aab96edc-6a38-49b5-a74e-e16ff8076fb5"
      },
      "cell_type": "code",
      "source": [
        "x"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'Const_2:0' shape=(32,) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 9
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      ]
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    {
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      "cell_type": "code",
      "source": [
        "y"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'Const_3:0' shape=(3,) dtype=int32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "EU5OlrdReRA3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sess = tf.Session() #start a new Session Object\n",
        "x_data = np.array([[1.,2.,3.],\n",
        "[3.,2.,6.]]) # 2x3 matrix\n",
        "x = tf.convert_to_tensor(x_data, dtype=tf.float32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vwZ6Wsy0eRDg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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        "outputId": "a68797b5-4ba6-4e38-c245-2db587dbf1c2"
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      "cell_type": "code",
      "source": [
        "x"
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      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'Const_4:0' shape=(2, 3) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "34wJTz8jeRGX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3d6d4336-1d4c-4ee3-c036-01284c020028"
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/bin/bash: -c: line 0: syntax error near unexpected token `https://images.gitbook.cn/1cb5a360-e0d2-11e8-b193-a92eb910d005'\n",
            "/bin/bash: -c: line 0: `[](https://images.gitbook.cn/1cb5a360-e0d2-11e8-b193-a92eb910d005)'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2Lt7-owpgKEa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 图中的节点（node）表示操作（operation），而边（edge）表示各操作之间流通的数据。\n",
        "![](https://images.gitbook.cn/1cb5a360-e0d2-11e8-b193-a92eb910d005)"
      ]
    },
    {
      "metadata": {
        "id": "rlB4IqoFeRI-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sess = tf.InteractiveSession()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2M-3vUSveRLW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = tf.constant([[2, 5, 3, -5],\n",
        "                 [0, 3,-2, 5],\n",
        "                 [4, 3, 5, 3],\n",
        "                 [6, 1, 4, 0]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zy6a8VNmeRN-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "ca946176-7e6d-4c7a-eba7-0b95f34ffd89"
      },
      "cell_type": "code",
      "source": [
        "tf.transpose(x).eval()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  0,  4,  6],\n",
              "       [ 5,  3,  3,  1],\n",
              "       [ 3, -2,  5,  4],\n",
              "       [-5,  5,  3,  0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "4yIXRu_2eRQS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y = tf.constant([[4, -7, 4, -3, 4],\n",
        "               [6, 4,-7, 4, 7],\n",
        "               [2, 3, 2, 1, 4],\n",
        "               [1, 5, 5, 5, 2]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "C132KUx2eRTL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "9d78693e-2c51-4a02-964d-70ff78aa6517"
      },
      "cell_type": "code",
      "source": [
        "tf.matmul(x,y).eval()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 39, -10, -46,  -8,  45],\n",
              "       [ 19,  31,   0,  35,  23],\n",
              "       [ 47,  14,  20,  20,  63],\n",
              "       [ 38, -26,  25, -10,  47]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "mTSNIXHCeRVg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e31325c6-113c-40e9-b5d9-6c816b54b141"
      },
      "cell_type": "code",
      "source": [
        "a = tf.constant([[1,2]])\n",
        "b = tf.constant([[3,4],\n",
        "                 [5,6]])\n",
        "tf.matmul(a,b).eval()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[13, 16]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "EBA5kN_oeRYB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f224e22b-eed5-4000-edca-b23e6f32d384"
      },
      "cell_type": "code",
      "source": [
        "1*3 + 2*5 , 1*4 + 2 * 6"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "5Nqs9svteRap",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d1df7c8-7ad9-4da1-8a01-7c5f3c9143a3"
      },
      "cell_type": "code",
      "source": [
        "floatx = tf.constant([[2., 5., 3., -5.],\n",
        "                       [0., 3.,-2., 5.],\n",
        "                       [4., 3., 5., 3.],\n",
        "                       [6., 1., 4., 0.]])\n",
        "\n",
        "tf.matrix_determinant(floatx).eval()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "818.0001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "CsSJKgc2eRdD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e2f189e2-17da-428f-94bf-fa1557fac5d5"
      },
      "cell_type": "code",
      "source": [
        "b = tf.constant([[3.0,4.0],\n",
        "                 [5.0,6.0]])\n",
        "tf.matrix_determinant(b).eval()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.9999993"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "TodHewlkAU5q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "逆矩阵（inverse matrix），又称反矩阵。在线性代数中，给定一个n阶方阵 {\\displaystyle \\mathbf {A} } \\mathbf{A}，若存在一n阶方阵 {\\displaystyle \\mathbf {B} } \\mathbf {B} ，使得 {\\displaystyle \\mathbf {AB} =\\mathbf {BA} =\\mathbf {I} _{n}} \\mathbf{AB}=\\mathbf{BA}=\\mathbf{I}_n，其中 {\\displaystyle \\mathbf {I} _{n}} \\mathbf{I}_n为n阶单位矩阵，则称 {\\displaystyle \\mathbf {A} } \\mathbf{A} 是可逆的，且 {\\displaystyle \\mathbf {B} } \\mathbf {B} 是 {\\displaystyle \\mathbf {A} } \\mathbf{A}的逆矩阵，记作 {\\displaystyle \\mathbf {A} ^{-1}} \\mathbf {A} ^{-1}。\n",
        "\n",
        "只有方阵（n×n的矩阵）才可能有逆矩阵。若方阵 {\\displaystyle \\mathbf {A} } \\mathbf{A}的逆矩阵存在，则称 {\\displaystyle \\mathbf {A} } \\mathbf{A}为非奇异方阵或可逆方阵。\n",
        "'''\n",
        "c = tf.matrix_inverse(b).eval() # 逆矩陣（inverse matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kTzQ34mXGFfN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "e2ce9084-1eb9-4457-bb92-509386ba7e31"
      },
      "cell_type": "code",
      "source": [
        "tf.matrix_solve(floatx, [[1],[1],[1],[1]]).eval() # 解矩阵，\n",
        "'''\n",
        "设x1=2, x2=3，列方程组：\n",
        "\n",
        "3x + 2y = 12\n",
        "\n",
        "x + y = 5\n",
        "解方程\n",
        "\n",
        "得到系数矩阵[[3.,2.],[1.,1.]]和值矩阵[[12.],[5.]]。\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "sess = tf.InteractiveSession()\n",
        "\n",
        "a = tf.constant([[3.,2.],\n",
        "                [1.,1.]])\n",
        "print(tf.matrix_solve(a, [[12.],[5.]]).eval())\n",
        "\n",
        "'''"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n设x1=2, x2=3，列方程组：\\n\\n3x + 2y = 12\\n\\nx + y = 5\\n\\n得到系数矩阵[[3.,2.],[1.,1.]]和值矩阵[[12.],[5.]]。\\n\\nimport tensorflow as tf\\n\\nsess = tf.InteractiveSession()\\n\\na = tf.constant([[3.,2.],\\n\\xa0 \\xa0 \\xa0 \\xa0 \\xa0 \\xa0 \\xa0 \\xa0 [1.,1.]])\\nprint(tf.matrix_solve(a, [[12.],[5.]]).eval())\\n\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "HnvQeolYGFmz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A0qErmxMGFpn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AVgi766AAe98",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b022b576-b3dd-4651-9440-87725346fa50"
      },
      "cell_type": "code",
      "source": [
        "np.matmul(b.eval(), c)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.0000000e+00, -4.7683716e-07],\n",
              "       [ 0.0000000e+00,  1.0000002e+00]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "t-RSkQZK8_Ot",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 矩阵行列式计算\n",
        "![](https://wikimedia.org/api/rest_v1/media/math/render/svg/4cb034bba94ea8e4f0873ff10cd102e2812edbd0)\n",
        "![](https://wikimedia.org/api/rest_v1/media/math/render/svg/f21caeb47d9a97321128e8f384255b6d5453edb4)\n",
        "![](https://upload.wikimedia.org/wikipedia/commons/4/4d/Determinant-columns.png)\n",
        "![](https://upload.wikimedia.org/wikipedia/commons/d/db/Parallelogramme.JPG)\n",
        "\n",
        "![](https://wikimedia.org/api/rest_v1/media/math/render/svg/78513743f73b477d803ae8d084e8e2ddb9d07933) "
      ]
    },
    {
      "metadata": {
        "id": "E9iwl2mXeRf5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# 约简（reduction）是一种跨维度张量操作，计算结果比原张量缩减一个维度\n",
        "x = tf.constant([[1, 2, 3],\n",
        "              [3, 2, 1],\n",
        "              [-1,-2,-3]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7eJ4_QRWK0N6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "boolean_tensor = tf.constant([[True, False, True],\n",
        "              [False, False, True],\n",
        "              [True, False, False]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lZk9NdMtK0S1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7088868e-a030-494b-826e-0a445d2942a1"
      },
      "cell_type": "code",
      "source": [
        "tf.reduce_prod(x, reduction_indices=1).eval() # 计算乘积"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 6,  6, -6], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "FrTxJz1YK0Vz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "84eaaf1a-36c0-449d-d281-0e6a4c5a67a8"
      },
      "cell_type": "code",
      "source": [
        "tf.reduce_min(x, reduction_indices=1).eval() # 最小值"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  1, -3], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "yyhye_CvK0YV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "64586a9e-ba33-462c-d66e-65fedaf3a369"
      },
      "cell_type": "code",
      "source": [
        "tf.reduce_max(x, reduction_indices=1).eval() # 最大值"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3,  3, -1], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "siWrjAPvK0bH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9213766a-7f5e-4269-89e8-de8dddedacb2"
      },
      "cell_type": "code",
      "source": [
        "tf.reduce_mean(x, reduction_indices=1).eval() # 均值"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  2, -2], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "iqVa4SyQLOUP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e88e22cb-7694-44f6-8212-a7f261a9a6e2"
      },
      "cell_type": "code",
      "source": [
        "tf.reduce_all(boolean_tensor, reduction_indices=1).eval()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "metadata": {
        "id": "SFDnUnh7LOXC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fdc933a2-2c56-4bef-f7f6-9c7561a3ccc7"
      },
      "cell_type": "code",
      "source": [
        "tf.reduce_any(boolean_tensor, reduction_indices=1).eval()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "RDzntM2nLOZi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "85373205-f1a4-4adf-dd31-f06348f65245"
      },
      "cell_type": "code",
      "source": [
        "# 张量分割\n",
        "\n",
        "seg_ids = tf.constant([0,1,1,2,2])\n",
        "tens1 = tf.constant([[2, 5, 3, -5],\n",
        "                [0, 3,-2, 5],\n",
        "                [4, 3, 5, 3],\n",
        "                [6, 1, 4, 0],\n",
        "                [6, 1, 4, 0]])\n",
        "tf.segment_sum(tens1, seg_ids).eval()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  5,  3, -5],\n",
              "       [ 4,  6,  3,  8],\n",
              "       [12,  2,  8,  0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "EFCjSiZbLOb7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "5f15c084-25ca-4f8e-83de-1d82ac0eb179"
      },
      "cell_type": "code",
      "source": [
        "# 将索引值相同的进行求和，其余的按顺序计算\n",
        "c = tf.constant([[1,2,3,4], \n",
        "                 [-1,-2,-3,-4], \n",
        "                 [5,6,7,8]])\n",
        "tf.segment_sum(c, tf.constant([0, 2, 2])).eval()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3, 4],\n",
              "       [0, 0, 0, 0],\n",
              "       [4, 4, 4, 4]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "metadata": {
        "id": "p4S2Fz19LOeU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6b80f5f1-5452-4ae1-a05a-5d62c49b60a8"
      },
      "cell_type": "code",
      "source": [
        "tf.segment_sum(c, tf.constant([0, 1, 1])).eval()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3, 4],\n",
              "       [4, 4, 4, 4]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "id": "_WloJ3u-LOgf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "960a24ab-f577-4eab-e050-64c18d075145"
      },
      "cell_type": "code",
      "source": [
        "tf.segment_sum(c, tf.constant([0, 1, 3])).eval()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2,  3,  4],\n",
              "       [-1, -2, -3, -4],\n",
              "       [ 0,  0,  0,  0],\n",
              "       [ 5,  6,  7,  8]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "metadata": {
        "id": "DCAHwvQ9LOkf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f882a92a-c3d4-4034-b832-abc04749d392"
      },
      "cell_type": "code",
      "source": [
        "tf.segment_sum(c, tf.constant([1, 1, 1])).eval()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 0],\n",
              "       [5, 6, 7, 8]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "metadata": {
        "id": "fStv6IzNLOnK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# 序列实用程序包括诸如 argmin 和 argmax（显示维度的最小和最大值），\n",
        "# listdiff（显示列表之间的交集的补码），\n",
        "# where（显示张量上的真实值的索引）和 unique（在列表上去除重复的元素）。"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "umvl2HTrLOpa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = tf.constant([[2, 5, 3, -5],\n",
        "             [0, 3,-2, 5],\n",
        "             [4, 3, 5, 3],\n",
        "             [6, 1, 4, 0]])\n",
        "listx = tf.constant([1,2,3,4,5,6,7,8])\n",
        "listy = tf.constant([4,5,8,9])\n",
        "boolx = tf.constant([[True,False], [False,True]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QliytbzrLOrv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "228307b2-6b23-4483-8685-0b4e3c10fd09"
      },
      "cell_type": "code",
      "source": [
        "tf.argmin(x, 1).eval()  # 返回最大值最小值的 索引"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 2, 1, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "metadata": {
        "id": "1AzP3ySmLOv1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2423e3c0-ff87-42f3-f4ff-c1adcd17abc0"
      },
      "cell_type": "code",
      "source": [
        "tf.argmax(x, 1).eval()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3, 2, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "metadata": {
        "id": "M79aKU7uLOyh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# tf.listdiff(listx, listy)[0].eval() # 不存在了\n",
        "\n",
        "# tf.setdiff1d(listx, listy)[0].eval() # 准备废弃\n",
        "\n",
        "tf.sets.difference(listx, listy)[0].eval()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3uDBv0wZLO0r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "235c7c2c-856e-46f6-a885-a236376e7d35"
      },
      "cell_type": "code",
      "source": [
        "tf.where(boolx).eval() "
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0],\n",
              "       [1, 1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "metadata": {
        "id": "10LNtHg_LO5I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dd645445-87de-49fa-c9a7-d7f0162b7c38"
      },
      "cell_type": "code",
      "source": [
        "tf.unique(listx)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unique(y=<tf.Tensor 'Unique_4:0' shape=(?,) dtype=int32>, idx=<tf.Tensor 'Unique_4:1' shape=(8,) dtype=int32>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "metadata": {
        "id": "8hhkS0ZSLO7e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "afc9d7e7-d9d5-4fb5-c96d-393e01de1cbe"
      },
      "cell_type": "code",
      "source": [
        "# 张量形状变换\n",
        "tf.shape(x).eval()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 4], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "metadata": {
        "id": "e0U0NNtVLPCI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "f4e8da51-8637-4556-e06c-993da4434232"
      },
      "cell_type": "code",
      "source": [
        "x.eval()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  5,  3, -5],\n",
              "       [ 0,  3, -2,  5],\n",
              "       [ 4,  3,  5,  3],\n",
              "       [ 6,  1,  4,  0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "metadata": {
        "id": "QNOsL0rNLPEy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e2a3494c-614c-4d93-d351-7af5bbee0b6d"
      },
      "cell_type": "code",
      "source": [
        "tf.size(x).eval()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "metadata": {
        "id": "C1dw12MfLPHZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "53cde6eb-7359-4e18-e86e-8cb7d979101a"
      },
      "cell_type": "code",
      "source": [
        "tf.rank(x).eval() "
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "metadata": {
        "id": "48yJ-e7-LPKX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "f676ceeb-be91-4454-d70a-64203ec2b7e9"
      },
      "cell_type": "code",
      "source": [
        "tf.reshape(x, [8, 2]).eval()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  5],\n",
              "       [ 3, -5],\n",
              "       [ 0,  3],\n",
              "       [-2,  5],\n",
              "       [ 4,  3],\n",
              "       [ 5,  3],\n",
              "       [ 6,  1],\n",
              "       [ 4,  0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "metadata": {
        "id": "faDq9my3LPM_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1c9e9bf2-41dc-4678-b656-17b16732ec15"
      },
      "cell_type": "code",
      "source": [
        "tf.reshape(x, [1, 16]).eval()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  5,  3, -5,  0,  3, -2,  5,  4,  3,  5,  3,  6,  1,  4,  0]],\n",
              "      dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "metadata": {
        "id": "XP6jUAwuLPPe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "41ea8997-b360-4075-d398-152ffdaa402d"
      },
      "cell_type": "code",
      "source": [
        "tf.reshape(x, [16, 1]).eval()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2],\n",
              "       [ 5],\n",
              "       [ 3],\n",
              "       [-5],\n",
              "       [ 0],\n",
              "       [ 3],\n",
              "       [-2],\n",
              "       [ 5],\n",
              "       [ 4],\n",
              "       [ 3],\n",
              "       [ 5],\n",
              "       [ 3],\n",
              "       [ 6],\n",
              "       [ 1],\n",
              "       [ 4],\n",
              "       [ 0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "metadata": {
        "id": "N3yUQb8mLPT9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "9074839f-a3f0-44b4-d1e1-6e0afd9323c5"
      },
      "cell_type": "code",
      "source": [
        "tf.squeeze(x).eval()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  5,  3, -5],\n",
              "       [ 0,  3, -2,  5],\n",
              "       [ 4,  3,  5,  3],\n",
              "       [ 6,  1,  4,  0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "metadata": {
        "id": "u2Fsgfz0LPWs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "886b00c2-8acb-4bb6-f57a-62f89e95083b"
      },
      "cell_type": "code",
      "source": [
        "tf.expand_dims(x,1).eval()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 2,  5,  3, -5]],\n",
              "\n",
              "       [[ 0,  3, -2,  5]],\n",
              "\n",
              "       [[ 4,  3,  5,  3]],\n",
              "\n",
              "       [[ 6,  1,  4,  0]]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "metadata": {
        "id": "olLO3pK-LPbR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "t_matrix = tf.constant([[1,2,3],\n",
        "                        [4,5,6],\n",
        "                        [7,8,9]])\n",
        "t_array = tf.constant([1,2,3,4,9,8,6,5])\n",
        "t_array2= tf.constant([2,3,4,5,6,7,8,9])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CtxekaypLPdu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a955f88b-0f86-4ec4-bcf3-62c582ee79dc"
      },
      "cell_type": "code",
      "source": [
        "tf.slice(t_matrix, [1, 1], [2,2]).eval()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5, 6],\n",
              "       [8, 9]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "metadata": {
        "id": "swzKNqYALPgM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5cbf3c1c-1285-4674-d0ac-a61a34eb8359"
      },
      "cell_type": "code",
      "source": [
        "tf.slice(t_matrix, [0,0], [2,2]).eval()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2],\n",
              "       [4, 5]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "metadata": {
        "id": "uz6RSSqaLPlD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "178e3970-f067-4b26-9ec5-ea28eb8da3de"
      },
      "cell_type": "code",
      "source": [
        "tf.tile([1,2],[3]).eval()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 1, 2, 1, 2], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "metadata": {
        "id": "KWCck0zLLPp_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "93ed9795-554a-4c36-c100-bc6c4173c36d"
      },
      "cell_type": "code",
      "source": [
        "tf.pad(t_matrix, [[1,2],[3,2]]).eval()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 1, 2, 3, 0, 0],\n",
              "       [0, 0, 0, 4, 5, 6, 0, 0],\n",
              "       [0, 0, 0, 7, 8, 9, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "metadata": {
        "id": "v0xIBMwhLPsy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "247bea68-b42c-406f-90b6-202c01d514c5"
      },
      "cell_type": "code",
      "source": [
        "tf.concat(0, [t_array, t_array2]).eval()"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-96-7ea714489d34>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mt_array\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt_array2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(values, axis, name)\u001b[0m\n\u001b[1;32m   1252\u001b[0m           \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"concat_dim\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m           \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_is_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1254\u001b[0;31m               tensor_shape.scalar())\n\u001b[0m\u001b[1;32m   1255\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36massert_is_compatible_with\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \"\"\"\n\u001b[1;32m   1022\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1023\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Shapes %s and %s are incompatible\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1024\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mmost_specific_compatible_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Shapes (2, 8) and () are incompatible"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "ptO34H9JLPxE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "7eeb686e-26a6-4980-8ddc-997c7ade6405"
      },
      "cell_type": "code",
      "source": [
        "tf.unpack(t_matrix).eval()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-97-0114bbfa7102>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munpack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_matrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'unpack'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "SOGm1izlLPvi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2Ll8rEXleX6g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![](WX20190226-190259.png)"
      ]
    },
    {
      "metadata": {
        "id": "c9Mnw_HALPoQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "5a8be530-5088-4774-d7ea-70c2c4abf893"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "with tf.Session() as sess:\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.plot(tf.random_normal([100]).eval(), tf.random_normal([100]).eval(),'o')\n",
        "    ax.set_title('Sample random plot for TensorFlow')\n",
        "    plt.savefig(\"result.png\")"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YnG4RDYxFu0u1+L1d7p/3lZx0/0xAhtXopgYQdXWBsbisSrXGqLisSovf26Wi3aUR2+ea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\nyJMjW3hvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KuLGdLjnks6K",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![](https://images.gitbook.cn/b0199590-e0d6-11e8-af35-6d3000480069)"
      ]
    },
    {
      "metadata": {
        "id": "JAYkZGhILPjf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import sklearn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bHV5wsw_LPZX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "52dbdb73-35c5-4824-ec55-d789af88abd7"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.datasets.samples_generator import make_blobs\n",
        "\n",
        "centers = [(-2, -2), (-2, 1.5), (1.5, -2), (2, 1.5)]\n",
        "\n",
        "data, features =  make_blobs(n_samples=200, centers=centers, n_features = 2, cluster_std=0.8, shuffle=False, random_state=42)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(np.asarray(data).transpose()[0], np.asarray(data).transpose()[1], marker = 'o', s = 250)\n",
        "plt.plot()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aUFGW44g9aBHbJmZDhDPL8k7q0dHL6phBb2BsPIi3PzyFkx2DuPsWOdtczMY3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\nEDpDxpcgCIIgdIaML0EQBEHozP8PD70J2GoNaOYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yn6r6zxNrHlB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "e723fdbf-7926-41b2-999f-f39df73b3fa0"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import time\n",
        "\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.datasets.samples_generator import make_blobs\n",
        "from sklearn.datasets.samples_generator import make_circles\n",
        "\n",
        "DATA_TYPE = 'blobs'\n",
        "\n",
        "N = 200\n",
        "\n",
        "# Number of clusters, if we choose circles, only 2 will be enough\n",
        "if (DATA_TYPE == 'circle'):\n",
        "    K=2\n",
        "else:\n",
        "    K=4\n",
        "\n",
        "# Maximum number of iterations, if the conditions are not met\n",
        "MAX_ITERS = 1000\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "# 四个质心位置\n",
        "centers = [(-2, -2), (-2, 1.5), (1.5, -2), (2, 1.5)]\n",
        "if (DATA_TYPE == 'circle'):\n",
        "    # n_samples 是数据的数目，\n",
        "    # shuffle 指数据是否发乱，\n",
        "    # noise 是添加到圆形数据集上的随机噪声数据，\n",
        "    # random_state 是随机种子，\n",
        "    # factor 是环形数据间的比例因子。\n",
        "   data, features = make_circles(n_samples=200, shuffle=True, noise= 0.01, factor=0.4)\n",
        "else:\n",
        "    # n_samples 是数据的数目，\n",
        "    # n_features 是特征数据的列的数目（维度），\n",
        "    # centers 是类的中心，\n",
        "    # cluster_std 是标准差，\n",
        "    # center_box 是随机生成数据中心时中心的边界，\n",
        "    # shuffle 指是否打乱样品，\n",
        "    # random_state 是随机种子\n",
        "    # 生成中心点附近的随机点\n",
        "   data, features = make_blobs (n_samples=200, centers=centers, n_features = 2, cluster_std=0.8, shuffle=False, random_state=42)\n",
        "\n",
        "# 初始化画布\n",
        "fig, ax = plt.subplots()\n",
        "# 绘制散点，质心x坐标，y坐标\n",
        "ax.scatter(np.asarray(centers).transpose()[0], np.asarray(centers).transpose()[1], marker = 'o', s = 250)\n",
        "plt.show()\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gAADTjd/NRufPn9fhw4dVVFQ07HhPT4/27dun48ePKysrS5s2bVJlZaXmzZs3rmIBAJgO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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "I91xXbpnr3hm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "4fa4a8fd-0f6d-4c42-b8ec-18df9efbef71"
      },
      "cell_type": "code",
      "source": [
        "features"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,\n",
              "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
              "       3, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "metadata": {
        "id": "DkiySQFwrq-F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "8f6fed6f-194f-40d0-9e8f-7045a6902e1e"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "fig, ax = plt.subplots()\n",
        "if (DATA_TYPE == 'blobs'):\n",
        "    # 绘制质心\n",
        "   ax.scatter(np.asarray(centers).transpose()[0], np.asarray(centers).transpose()[1], marker = 'o', s = 250)\n",
        "    # 绘制数据\n",
        "   ax.scatter(data.transpose()[0], data.transpose()[1], marker = 'o', s = 100, c = features, cmap=plt.cm.coolwarm )\n",
        "   plt.plot()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wrFo5JXb03PB7un9yU1Z09xtX0jWqTzmxpPa1Z5/G4N8fYviRp0o6z/Pmo6g4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\nki8hhBASZpR8CSGEkDCj5EsIIYSE2f8DRMqXVQWPrwgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "shvKU7NpulKz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "d9f59281-bdad-48b8-b0ae-3c6aa5dbc455"
      },
      "cell_type": "code",
      "source": [
        "input = tf.constant(\n",
        "[\n",
        "    [\n",
        "        [1, 1, 1], \n",
        "        [2, 2, 2]\n",
        "    ],\n",
        "    [\n",
        "        [3, 3, 3], \n",
        "        [4, 4, 4]\n",
        "    ],\n",
        "    [\n",
        "        [5, 5, 5], \n",
        "        [6, 6, 6]\n",
        "    ]\n",
        "])\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "[1,0,0]表示第一维偏移了1\n",
        "则是从\n",
        "[\n",
        "    [\n",
        "        [3, 3, 3], \n",
        "        [4, 4, 4]\n",
        "    ],\n",
        "    [   \n",
        "        [5, 5, 5], \n",
        "        [6, 6, 6]\n",
        "    ]\n",
        "]\n",
        "中选取数据\n",
        "然后选取第一维的第一个，\n",
        "第二维的第一个数据，\n",
        "第三维的三个数据\n",
        "\"\"\"\n",
        "data1 = tf.slice(input, [1, 0, 0], [1, 2, 3])\n",
        "print(sess.run(data1))\n",
        "\n"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[3 3 3]\n",
            "  [4 4 4]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KEWTfspdot2D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e39f410f-1349-4c34-821b-d48457f24aae"
      },
      "cell_type": "code",
      "source": [
        "input = tf.constant(\n",
        "[\n",
        "    [1,11],\n",
        "    [2,22],\n",
        "    [3,33],\n",
        "    [4,44],\n",
        "    [5,55],\n",
        "    [6,66],\n",
        "    [7,77],\n",
        "])\n",
        "#[2,0]表示第一维偏移了1\n",
        "data1 = tf.slice(input, [2,0], [2,2])\n",
        "print(sess.run(data1))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 3 33]\n",
            " [ 4 44]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DERY81zTn3OK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7f0ec14e-650e-4748-b8ed-fd3150a5ef4a"
      },
      "cell_type": "code",
      "source": [
        "points.initialized_value()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'cond_1/Merge:0' shape=(200, 2) dtype=float64>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "RJUZBa3JpyWF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "85a37046-8358-49db-ff76-f3e0a49bbb8b"
      },
      "cell_type": "code",
      "source": [
        "data[:4]"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.60262868, -2.11061144],\n",
              "       [-1.48184917, -0.78157611],\n",
              "       [-2.1873227 , -2.18730957],\n",
              "       [-0.73662975, -1.38605222]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "Dfu0tJU5suYO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "6955cd26-da9e-453c-b5fe-442e638654b0"
      },
      "cell_type": "code",
      "source": [
        "# 定义做所有坐标点\n",
        "points=tf.Variable(data)\n",
        "print(points)\n",
        "\n",
        "# 生成 200 个初始值为0的张量，默认值0\n",
        "cluster_assignments = tf.Variable(tf.zeros([N], dtype=tf.int64))\n",
        "print(cluster_assignments)\n",
        "\n",
        "# 选前4个点作为质心\n",
        "centroids = tf.Variable(tf.slice(points.initialized_value(), [0,0], [K,2]))\n",
        "print(centroids)\n",
        "\n",
        "# 初始化会话\n",
        "sess = tf.Session()\n",
        "#sess.run(tf.initialize_all_variables())\n",
        "# 初始化全局变量\n",
        "sess.run(tf.global_variables_initializer())\n",
        "# 获取质心\n",
        "sess.run(centroids)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tf.Variable 'Variable_15:0' shape=(200, 2) dtype=float64_ref>\n",
            "<tf.Variable 'Variable_16:0' shape=(200,) dtype=int64_ref>\n",
            "<tf.Variable 'Variable_17:0' shape=(4, 2) dtype=float64_ref>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.60262868, -2.11061144],\n",
              "       [-1.48184917, -0.78157611],\n",
              "       [-2.1873227 , -2.18730957],\n",
              "       [-0.73662975, -1.38605222]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "_u6I759UxbAl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "85597941-71af-445f-e490-38de60648423"
      },
      "cell_type": "code",
      "source": [
        "K"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "8dL1ZDwzwnYX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "d62436de-70ef-4b1d-dedb-d7999635fcf1"
      },
      "cell_type": "code",
      "source": [
        "sess.run(tf.tile(centroids, [2, 1])) # 第一维复制2次，第二维复制1次"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.60262868, -2.11061144],\n",
              "       [-1.48184917, -0.78157611],\n",
              "       [-2.1873227 , -2.18730957],\n",
              "       [-0.73662975, -1.38605222],\n",
              "       [-1.60262868, -2.11061144],\n",
              "       [-1.48184917, -0.78157611],\n",
              "       [-2.1873227 , -2.18730957],\n",
              "       [-0.73662975, -1.38605222]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "FT_oD_vjw9PX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "86f7f759-bb21-4fcc-9da6-60e90b556f9d"
      },
      "cell_type": "code",
      "source": [
        "sess.run(tf.reshape(tf.tile(centroids, [2, 1]), [2,4,2])) # 第一维复制2次，第二维复制1次"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[-1.60262868, -2.11061144],\n",
              "        [-1.48184917, -0.78157611],\n",
              "        [-2.1873227 , -2.18730957],\n",
              "        [-0.73662975, -1.38605222]],\n",
              "\n",
              "       [[-1.60262868, -2.11061144],\n",
              "        [-1.48184917, -0.78157611],\n",
              "        [-2.1873227 , -2.18730957],\n",
              "        [-0.73662975, -1.38605222]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "4cs-vPh-LO3d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3322
        },
        "outputId": "287aadba-14f6-4f7e-cb33-7ae771417559"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# 复制质心 200 次，并行为 N ，K ，2 形状\n",
        "rep_centroids = tf.reshape(tf.tile(centroids, [N, 1]), [N, K, 2])\n",
        "# 复制点\n",
        "rep_points = tf.reshape(tf.tile(points, [1, K]), [N, K, 2])\n",
        "# 计算每个点到质心的欧几里得距离\n",
        "sum_squares = tf.reduce_sum(tf.square(rep_points - rep_centroids), reduction_indices=2) # 在第二个轴\n",
        "# 计算更好的质心，即找到最小的距离的\n",
        "best_centroids = tf.argmin(sum_squares, 1)\n",
        "\n",
        "did_assignments_change = tf.reduce_any(tf.not_equal(best_centroids, cluster_assignments))\n",
        "\n",
        "# \n",
        "def bucket_mean(data, bucket_ids, num_buckets):\n",
        "    total = tf.unsorted_segment_sum(data, bucket_ids, num_buckets)\n",
        "    count = tf.unsorted_segment_sum(tf.ones_like(data), bucket_ids, num_buckets)\n",
        "    return total / count\n",
        "\n",
        "means = bucket_mean(points, best_centroids, K)\n",
        "with tf.control_dependencies([did_assignments_change]):\n",
        "    do_updates = tf.group(\n",
        "    centroids.assign(means),\n",
        "    cluster_assignments.assign(best_centroids))\n",
        "\n",
        "changed = True\n",
        "iters = 0\n",
        "fig, ax = plt.subplots()\n",
        "if (DATA_TYPE == 'blobs'):\n",
        "    colourindexes=[2,1,4,3]\n",
        "else:\n",
        "    colourindexes=[2,1]\n",
        "while changed and iters < MAX_ITERS:\n",
        "    fig, ax = plt.subplots()\n",
        "    iters += 1\n",
        "    [changed, _] = sess.run([did_assignments_change, do_updates])\n",
        "    [centers, assignments] = sess.run([centroids, cluster_assignments])\n",
        "    ax.scatter(sess.run(points).transpose()[0], sess.run(points).transpose()[1],  \n",
        " marker = 'o', s = 200, c = assignments, cmap=plt.cm.coolwarm )\n",
        "    ax.scatter(centers[:,0],centers[:,1], marker = '^', s = 550, c = colourindexes, cmap=plt.cm.plasma)\n",
        "    ax.set_title('Iteration ' + str(iters))\n",
        "    plt.savefig(\"kmeans\" + str(iters) +\".png\")\n",
        "ax.scatter(sess.run(points).transpose()[0], sess.run(points).transpose()[1], marker = 'o', s = 200, c = assignments, cmap=plt.cm.coolwarm )\n",
        "plt.show()\n",
        "end = time.time()\n",
        "print (\"Found in %.2f seconds\" % (end-start)), iters, \"iterations\"\n",
        "print (\"Centroids:\")\n",
        "print (centers)\n",
        "print (\"Cluster assignments:\", assignments)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k5GQajcZxzx08eDDNZrOGMQFgYWkZ6L6+vuzYsSNJMj4+nmazma6uriTJypUrMzMzk/fe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\njBpYUMuoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9iVV/Oak0UnynEUMxrYDwlkYhsXI7LCLp8vtxo2mVQFyb0Po1S2qsnkNTy29p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YwfY0SnVeG0OFGc/n3qrt9YyppPzuJSwCG3frdcq5W4NyeDW8YEriBCMPnyXPvhlw9i3rHP68fq\n9epfAtpgvvG11H13mLJ3P8SSfwLFZkcVqUM/4Aq63DudyF7dvB7bbbNz/PFnqfx8K9ibmXeRJKKG\nDKTHwnnor+yLs66e479/muqt39DSakJt13S6Pv0oCRNubPN6/MFttZH/wBxqNn/d6nHajFS6/88c\n4m4a4bfHDsv3tFa0dT1GY8szZOKbb4DZMwahqDVoyo8h25sW01cAlz4BW8Zg3NHh/0YecXJ3m4kX\nQOWwoDv9LRatHkXftJORJEkMGxLHsCHNb+FRR/r3qetwuNm6rwarh3Vs7XaFTV9WcPOoJFShLNAf\nQnYvOhY1Zj0evApU0VkDiM4a4Jex3A4nBffPprq1pKUo1O87SP79c+jz5gvo+/emz9t/o2bbTsrX\nfEbdvoO46xpAoyaiazqxN91Ayr3TkaObvxUTCKoIHX2W/43il9+kZvPX59oqNvoOpjYmYhgxlJQH\n7yJ6UAB7kV/mRPINAkdKfxzGPmhN+ajMpnNdjSQJRaPHEZ95Uf3jFikKsrkUVUM1kqLg1uhwJnQD\nVeCm77ylqi1FXev5UkrZ3oC2rABbd++qJwXCpi/LKT7r3Zal4ycb2LG3ihHDEto+uDPysTGB4vau\nx29HUfzC0tYTbyO2E0WcWrCQfmvfQJIk4sYOJ27scBRFwW2xodKqW+zgFAwqjZrMJx8i4/8+QOX6\nTTT8eBwcTuTYGJKmT0JrvLxvqQSDSL7BIquxp/SHlP7enae40ZT+iLqmGLm+8qLpa1fpEZwxKThS\n+qFoA1N1yxvaipNtTq9fSjafBZcD5MDd63K5Fb7cWcGhI3U/TWGrSE/Rcdu4ZPSR5z68HD7q/SpW\nBfjukPmyTb5ynG+LzjSJnvVs7kjcDidVm7/y6hzzvoPUbNlB3E03XPidJEnI+sCVqPWWpFKRePst\neJtqFacTe0kZrrp61PFxaLokieYLXhLJtwNT3C4iju9EU9N8VRrZVodsK0BdV4al+zAUfWiTgKq+\nwutzZIcFTcUpHMm92z7YB+s3lrL1mwpOFjVd3blpezlXX2Xgt3dmYrH69m3M6uN5nYHhuiEerShu\nTJuZRlLO5ABF5BvrydOULl9F/cEjuC1WJK2GyN7dScqZjOHaqwEoz/0M65Fj3g3scFK+9vOLkm+4\ns50uoXT5Kqq378JacAqcTqTICKIG9yf+5lEk/zq7Q3246MhE8u3AHAe/bDHxNiZba4k8mYel96jQ\nfQNW3F7vaT5PcgWmDOE7q0/z6eYyXC3kR1Olg43bKzhrsvvc4k+tuXwrtHb5bQ6m3E9xtdKr91Kx\no4Yh633rBOVvbpudE7P/SvXGr5qUdazfd5CKjzcQc90Qerz4Jyw/epl4f2IrbH/p046i/MMvKPrL\nP5oUU1EsVup2fUvdrm+pWLeBnoufRX+FH3ZtdHKX7ztHB6cym3CXnvT4eNlai6b0SOACapOEIvn2\ndFIC0Kz7X1tNfNZK4m3swPdmSsp8K1GZYgxcwYiOTpeegjFnisdFYSL69iB11j0BjsozboeT/Ptm\nU5H7WYv1lBWbndrtu8j/zeNeVcy6aAxH8OtbB0Ll51s49dQLTRLvpRoOHKHg4fnYTp8NUmThSyTf\nDkpTcQI83B98nrq2FEK1mEWScOu834ivSDKuaP+211MUhW07Ki4qkNGWyio7+kjvXg6JcRom3hz+\nK9TbI3P+70m+5w5oY39qZL9e9Hz5WSK6dozqX6efX0LNvz1bPNVw6Efq9nzn0+PIMcFbxRwoitNJ\n8UvLPNqvDGA9cozTi14LcFThz6dp54qKCubMmYPNZsPhcDBv3jwGDx7s79guX4qCXOd9QwTZZkZd\nXYQzobv/Y/KAKy4NTV3b24wac0Yn4Y7y78rKvQdqKDjRdFtXa6w2hZRkDQ0Wz2tmZQ00EB0V2KII\nHZ0kSXT/y5PEDB1MxccbMO/ch6tRxauIfr2IG3M9KQ/ODGiTeG+4bXaqPFy1fJ69tBwidWDxboYk\n+ppBXh3fEZWv/QLL9/lenVP79R4cVTVo4r1rt3k58Sn5rl+/nilTpjBp0iTy8vJYvHgxb731lr9j\nu3wpLp/vg2oLv0NbfAhkNS59Ao6kHkHbP+xI6nWu1rXVs0/ICuCM939D9YPf1+HybtIAgNgYNVGR\nMsdOtd0TdvCAWO6b6f/Yw1XilPEkThmP5Xgh5l3/wW2zo0tNJm7ciJBuqWlO2cqPsB3zrn2kYrGi\nSeuCw1Lq8TlqYyJdwrw6lMtcR9m7a70+z3HWRNmKXNIfuy8AUXUOPr0q7rnn5/s2JSUldOkSuE4c\nlyVJ5XNzBdltB7cdHD/dB64qxGlIxdr9WpC9/++WrGY01UXgcoFKhTMmueVkrpKxZWQRcWoPKodn\nTc21ZQWoHFbsKVf6paEEgN3hQ+YFXG5Y8GhvXnn7JAeP1OFwNN02FaGTGDzAwH/PHkBDQ2Bq/Iaz\nyJ5diezZsfsVW7xsLHCeOj4WxWrDWenZArPE229BmxSe29DM+w5ieu9Dar7Kw3HG8w8cjTlM3u9+\nuJz4/JHUZDLx0EMPUV9fz4oVK9o8Pj5ej1odnIIQrZX0Chc2fTRKbfvf3CXFjaamGG3RLrTXjEfy\nsCiHy1SE6/QR3JVnwfnzVKyu9AiquC6oUnuhzujb9ERjDK64KJxH81DMrb/4JEC21iCX1BAp2dAM\nHOWXvYKxBt/65UbpNfTtk8A/nk9g/+EaPt1UwolT9dgdbiJ0Mr16RDHl1jT69T73/IrqZFPOneF1\nc15r13LGx6dYRFw0vR/9NUfm/w1HeVXLB0oS6TMnM/iVPyH5aTFhMP9vTr62koJn/4HDww8ZLXGX\nlDYbd2d6noHv19Nm8s3NzSU3N/ei382aNYuRI0eydu1atm/fzrx589qcdq6q8u4enK86S+1QrT4Z\nXa33931bolSeoXb/DuwZWW0eqzbloztzCFVzU99uF+7KM7gqS2goO4stI6uZb6wx0HssuhO70VQX\n4cl7netMARaXGnt6+++RdUvXIkkXVczzSGqy5sJzJy1ZxQMzm18cZDKZO83z7LzOdD1tXYvdhxkg\nALc2gsiJt9DDEEfZO7mYd+zDVdvocdQy0YMHEH/bjaQ8OJPyinqfHudSwfy/MX3wKaeeehF3Xftj\nr/x6Lyc27bqovGdnep5BgGs7Z2dnk52dfdHv8vLyqKmpITY2ltGjRzN79mwvwhU8YTf2QVd1Emz+\n+9Ciri3B7r6q1ZKUctXplhNvIxIKGlMBilqLPbX52rmytdajxHtuPFBXncaeeiWo2neP8Lpr4ujV\nXe/VoqvICBW3ju0YC4KEwIofP5Ky9z4Eu3frKqKGDAQgbtS1xI26FsvxQio/3oC7wYKk0xKVdSVx\n40aGbaUnxenk7P++55fEC+Cua+DsG/+k92t/9ct4nY1PcyIbN27ko48+AuDo0aOkpqb6NSgB0OhQ\n97kGpZ2JqDHZakZTcbLVY7Sm/DYT73kSCuqKU9BMcQ1NxUlka4138dnr0Jh8K2bQmEolMWJYPN7M\n+A2+MoZuGR2j+IMQWIaR1xJ99UCvztGkJtPlvosXT0X27Er6H+8n86k/kPHkQ8Tf7J/bJs1RFAV3\nC12R/MX0wWdYfijw65i1X+/BXupbA47Ozqd39kceeYS5c+eyadMm7HY7zzzzjJ/DEgDU6X0xm61o\nzxxC9nABU1skW8tTJCpzKXKdd4skzidMR5crLh6rzrcXnFxXgcMP6/cmj0/mzFkrm76saHP6uV/v\nKH5/r/ft5YTwJEkSyXdNpeHQEdz1nr2uEqeMRxMb3HuVrgYrZStyKdi+A3N+IYrTiRytJ3rIVSRN\nn0TsCP82JPF037M3nOWVlH/wKWkdpLhKR+JT8k1ISOCNN97wdyxCM5yJPXDGZaAty0euKUFyNCAp\nILlsSF4W4QBo0p2+EXV1MRLejynXmZokX8nXYh8+lqi8lCRJPPSrrsTHavhydyUlpU337xqiZbIG\nGHjw7kz0+o61HUYIrKRpE7CfKeXMP95qPQFLEonTJpD51B+CFxxQ/e+vKXzm71gv2RLlNFVgO1FE\nxSebibtpOL2WPOe3cp3Oau9mqjzVeN+38DPxjhMOZM25e6GpV15YRRSZvw21L98u1S2v0PV1b7Hk\nbKYwha+rPP3YIlGSJHJuT2PqhBQ2bDVx5Fg9NpsLtUZFWrKO225OJin+8i0PeblLm3UP2ow0ylet\nw7xnP4q1UQENlQr9VVeQcNtNpP7u10G9j1u9dSfH//gczta26tjtVH+xjfzfPkHfFS+jaqPCmEcC\ndI2SP2LrhETyDTc/vUBcUUleJ1+3rMER38r0qs+1mZsmTHeEwaex3BH+n9rTaVVMvqULHauXjtAR\nJE29haSpt1C7Zz/VG7/E3WBBpdMSlTWAhIk3+W2rkKcUp5OivyxuPfE2Urt9N8UvLCVzwax2P7Ym\nOQA9fCWJyH6B6VgW7kTyDVP25L5oKk+gcni+F9gVbUSJbDkpKj7UZgaa7aRkN/ZBbTqO7PB8xbFb\nHYEj+dze4fIqO5u2l1NX50SSJBITNNw6xkhkZHD2iguXF8PQwRiGhr5ErumDT71e9FS9ZQcZcx5u\ndyWxhInjqPxkMz6Vh2uBflB/Em670W/jdSYi+YYrjQ5HfDe0ZUc92s7TOLG1xG7sjdp0zKuEqUgy\njsSeTf9C1uCKTUMu9/yNxGnowokSN7mfHefQETO15ovvG3+xxcTgKw3c/cs0DDFiKkvofKr+tc3r\ncyw/5FP+4b8w3jGxXY8d/3/GEjWoP/XfHm7XOBeNOX5U0GcPwoX4Vwlj9vRBOBJ7orSRft3qCKwZ\nWbhiklsfUNbgMqR4FYMrOgl3dPPTVbaMwTg8HM8ZbWRvwxUsfO04O/ZUN0m8AKYKB5u/quC5vxdQ\nVu5bC0BB6Mjsxb6Vcrx0YZYvJEkiffbDqI3+mX6Ou3UMaX8Qq5xbIpJvOJMkbF2vwZo5BEd0cpN+\num51BI74rlh63oArwbN6u7aMLJxRnrX4c+lisGYOafkAlYy15w3YE3rglpsv+eiWtdjju1IYfy2v\nv1vM2bK2uwoVnLTw8rKTuFxelrAShI7OkwbU/jzvEnGjr6PHCwvQZaa1epyka7mEqypaT+IvJ9B7\n6f8gyf69TVS7+1uKX36TokWvc2bJ21gLz/h1/GAS087hTpJwGnvhNPZCZTahrisH3CgqNc74bija\nCO/Gk9VYeo0g4tRu1LWlzW5nUgBXVCLWbkNR2logpZKxdR+K3d5AbH0h1vJSJLcLRSXjjozFYeyD\nooti/ftFlJR6/m32h/x6tu6oYNxI//YCFoRQkg2+rbtQ+Xhec+LHjyL6vwZx9s33qdmyA2vBSdw2\nG3KUHv1V/Yi/ZTRJ0ydTuW4DlV9sxV54BrfDiRwTRfSQq0j+1TSiBrR+i8tbZSs/ovLjjZj37kex\n/fwBvWTpuxiG/xfGu6cRN+pavz5moInk24m4Y4zYY/xQIlGtxdprJCqzCU3FCeSGqnP7b1Uq3BEG\nHPFdccVleLU1QdHq0aQPpTr+kiIfioLbVMiBb0sB7z4l79xbLZKv0KlED8uibu8Br85RJyVgnDHF\nr3FoEuLInP0wGU88iLOiCleDBbUhBnWj/rzJd/2C5Lt+4dfHvZSiKJx66oVz5UCbqfDlqqql6rMt\nmHf+h4x5vyN55tSAxuNPIvkKLXLHGLH5I5m3xGEj8vg37DtcT2F5800MWlNwoh6L1UVkhFgBLXQO\nKffPoHz1JzgrWumadAnDyKFo/XSf9lKSSoXGmEgwljc6Kio5++YqzDv24ayqRlLJuKw27EXF56bb\nWuGsrOb086+gMSYSP35UEKJtP5F8haBxmSvRFR5AZalGcjlQWc1IKNidTbcqecJidVPfIJKv0Hlo\nuxhJyr6Ns/+70qO2XNrMNFIfvjsIkQWOoigUv7QM07sf4ijzvZObs6qG0rdWh03yFQuuhMBzOYg4\n/g2O3Z+gLT+Gur7ip45H595cru/RwH9POEO01rvSkhqNCq1WPIWFziXzT49inHk7tNH/XNstne4L\n5xE1sF+QIguMor8u4czi5e1KvOeZ9+ynbp930/ahIt65hMByO4ks+BpNdTG4mk+uahmG92zg+Ukl\nRIj6BgIAAAi2SURBVHmRgFOTtUTrxbdeoXORJInuC+fTY+F8DGOua1K7WZuRinHmVPq+8xJxY64P\nUZT+UbVhO6XLV4HTP6u1FYv1XKGQMCCmnYWA0hX9B3W9Z2Uwr0y18eiYcp7f6Nne4CGDDKhU4dk7\nVRBaI0kSxhlTMM6YgqbwJIVffIPicCDHGkj6xa3IUb7dquloytd8ftHqZX9w+qkfcaCJ5CsEjsOG\nuta7ogFDMhpIinJQXt/6Eg9jooaJ49ooGiIInUDcNVfh6No91GH4ne10CbU79vp9XJUmPKrfiWln\nIWC0pgJUXvYhjtO7mTq49dZmhmg190zPECUmBSGMmXd9i6vK/20Mdd0y/D5mIIhvvkLAqGzmtg9q\nRq/EloptKHTP1HPn1FSGZsX5HpggCCHnavDug7kndN0ySP7VL/0+biCI5CsEjtu3RRRXZ1p48AYT\n/ynSU2uVUUkKCXoX115tYPiEfsjiPq8ghD1dRqrfx4wdex2y3suqfiEikq8QMIrs27SwrILsq2vI\nvvrnKSm3SkP9gGEgEq8gdAqxo4YR2b8Plh/y/TJezPBr6Pqnx/wyVjCIe75CwLhiktsqTOMRBXAY\ne4MmPD7RCoLQNkmtJnbMde0fR6cl7pbR9Hn7RVSR4fMeIb75CgHjTOiKu+xHZEu1z2MonGs3aE8b\n6L/ABEHoEFIeuovqLd9gPXrco+PlxDjU8XEodgdyVCRRg/qTNGMKhmFZAY7U/0TyFQJHUmFP7E7E\n6QNINO2O1BYFcMakYO0THuXiBEHwjtaYSM+//5njjz6DNf9Eq8dGD8uiz5uL0CQlBCm6wBLTzkJA\nOZP7Yu/SF8XLp5pLG40t7SqsvUcGKDJBEDqC6KwB9F3xd5JmTEab3rTATkTfnnR5YCZX/HNJp0m8\nIL75CkFgTx+EWxeN3nwad1XphZrOcO7brVsdCWodbp0eVGpc0UYcST1AEp8NBeFyENE9g54vPo3L\nXEfZP9fhrKwClQpdZhpJ2RNRaTpfqup8VyR0SM6knuj6D6byWAFy7dlz25BUMq4YIy5Dqle9gQVB\n6JzkmGhSH5wZ6jCCQiRfIahchi64DF1CHYYgCEJIiXk9QRAEQQgykXwFQRAEIchE8hUEQRCEIBPJ\nVxAEQRCCTCRfQRAEQQgykXwFQRAEIcgkRVH8UfteEARBEAQPiW++giAIghBkIvkKgiAIQpCJ5CsI\ngiAIQSaSryAIgiAEmUi+giAIghBkIvkKgiAIQpB1yuRbXl7O0KFD2b17d6hDaZeKigruu+8+7r77\nbnJycti/f3+oQ/KZ0+lkzpw5zJgxgzvuuIO9e/eGOqR2y8vL4/rrr2fr1q2hDsVnzz//PNOnTycn\nJ4cDBw6EOpx2+/HHHxk3bhzvvfdeqEPxi0WLFjF9+nSmTZvGxo0bQx2OzywWC48++ih33XUX2dnZ\nYf2aOc9qtTJu3Dg+/PBDn87vlC0FFy1aRGZmZqjDaLf169czZcoUJk2aRF5eHosXL+att94KdVg+\nWbduHZGRkbz//vvk5+czb9481qxZE+qwfFZYWMjbb7/NkCFDQh2Kz/Ly8jh16hSrV6/m2LFjzJ8/\nn9WrV4c6LJ81NDTw3HPPcf3114c6FL/YtWsX+fn5rF69mqqqKqZOncr48eNDHZZPtm7dysCBA7n/\n/vspLi7m3nvvZezYsaEOq11ef/11YmNjfT6/0yXfnTt3EhUVRd++fUMdSrvdc889F34uKSmhS5fw\n7YM7efJkJk6cCEBCQgLV1dUhjqh9jEYjr7zyCgsWLAh1KD7buXMn48aNA6BXr17U1NRQV1dHdHR0\niCPzjVarZdmyZSxbtizUofjF0KFDGTRoEAAGgwGLxYLL5UKW5RBH5r0JEyZc+Dnc38sAjh07RkFB\nAWPGjPF5jE417Wy323n11Vd5/PHHQx2K35hMJqZNm8brr7/OY489FupwfKbRaNDpdACsWLHiQiIO\nV5GRkWH5JthYeXk58fHxF/6ckJCAyWQKYUTto1ariYiICHUYfiPLMnq9HoA1a9YwatSosH/O5eTk\n8MQTTzB//vxQh9IuCxcuZO7cue0aI2y/+ebm5pKbm3vR70aNGkV2djYGgyFEUfmuueuZNWsWI0eO\nZO3atWzfvp158+aFxbRza9eycuVKDh8+zNKlS0MUnfdau57ORFSa7Zg2b97MmjVrwuK135ZVq1bx\nww8/8OSTT7J+/XokSQp1SF77+OOPycrKavetzbBNvtnZ2WRnZ1/0u5ycHNxuNytXrqSwsJADBw6w\nePFi+vTpE6IoPdfc9eTl5VFTU0NsbCyjR49m9uzZIYrOO81dC5xLYlu2bOG1115Do9GEIDLftHQ9\n4S45OZny8vILfy4rK8NoNIYwIuFSX331FUuXLuXNN98kJiYm1OH47NChQyQmJpKamkr//v1xuVxU\nVlaSmJgY6tC8tm3bNoqKiti2bRtnz55Fq9WSkpLC8OHDvRonbJNvc1atWnXh57lz5zJ16tSwSLwt\n2bhxI99//z2/+c1vOHr0KKmpqaEOyWdFRUWsWrWK995778L0sxBaN9xwA0uWLCEnJ4fDhw+TnJwc\ntvd7OyOz2cyiRYt45513iIuLC3U47bJ3716Ki4tZsGAB5eXlNDQ0XHTLI5y8/PLLF35esmQJ6enp\nXide6GTJt7N55JFHmDt3Lps2bcJut/PMM8+EOiSf5ebmUl1dzQMPPHDhd8uXL0er1YYwKt9t27aN\n5cuXc/z4cQ4fPsy7774bdtOCQ4YMYcCAAeTk5CBJEn/+859DHVK7HDp0iIULF1JcXIxarWbDhg0s\nWbIkbBPX559/TlVV1UVrPRYuXEhaWloIo/JNTk4OCxYs4M4778RqtfL000+jUnWqJUdeEy0FBUEQ\nBCHILu+PHoIgCIIQAiL5CoIgCEKQieQrCIIgCEEmkq8gCIIgBJlIvoIgCIIQZCL5CoIgCEKQieQr\nCIIgCEEmkq8gCIIgBNn/B9oLoG0PzqbDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/fRoNZ36MoUeeJvTUC+iRGLJDxdHeQssXzqp4E3s9GqP3d/eQWLsRI5kiGwiS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\nIvkKgiAIQpj9f4FTHXMtVCSdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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NY5wL98cUW/fN6DL9MYX2mtKf/e4IOooS3l0MxFRC/iy1HrEEFhSm8TOn03DW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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N0fvbe0is34SRTJENBElt2ma5n8EHHifb3VsGC99BqfXTcuF5ZR2j0gjxnWTs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v8an0/BGK/hojwT06ekMGVCckQW7WSGOJc+8xorOq2Gz3c3cqqyDc+/zmvYECMXjzLx93U1NDZf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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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ixHcS0h1VaA+phFNgmINXew6sfnMTWravXJmCdKO4h5gZPbtFrMpp6sY4F+6O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\n2DRhbZeLvnj+s+NCcSgGLSX0EO6NFb7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SmaPKYjegCq/J+qYMJ2IKA0mJRO71xgpeWafK\nZ9BUruI/adA7hmQ6Ii5bPXxDSZmB4xIuycTnMgmWaTQE9HH1B5bV5kirAkOZyf+EK73aqcYTopBv\nRHEoZDKQlFCN0QZeEQ1aakUW+Kdm/9mh9CR27aX9jn8lOYakY2rPAQYeeJSqN72Bxf/294hu+wbP\nKuOVwEzW6x2mYqGfqnWNeK97O82f+dikzzcfKFu1nHXP3E/3v99FZPMzZA6dpsQlCPjWrKDijZfT\n9Klbp7b8yTSnRx5+ujLc5oq8ZDFYvZesCl0xhZwuYJr5rOX6gDZm4pNVTBNSOYGMJqBIJmUuc8Te\nsWbAi51eslqpYrAmAbfBimB23PnrBhzodzOYkmyXCYHJ4qosrdWj655zGpyIKiRyIoaZN8x+t8GC\ncpXmxvnzO4P5/XeTeGUvRz7xRXId3QXHVl63ieV3fX3a1LnU0AC7N70XPTbys194VQPNFwVLcg2t\neRXC+z5fknNNltn2OzN1ncHfbznZJlLAs2gBVW+9CkEqbrttMvKSjuc7yzBM6I7KRNIiYr+Bprlx\nyyaN5SoVnomNqFuBJTVTEwYVhLyXPV7Yuisql9DwAgjEsxL7et2sbciOeV1JhDUNWVI5gRNRmWTu\nZOs/AZKqgDaG1zrWdbpiLqp8GcrP+Hxd8tR9ng7Tg2matP/d14syvABDm//IiW98n5Yv/J8pnlke\nJVhD4PILGfrD06deK5XXe+p8vUcw2vdA69qSnXO+IEgSNe+4bkau7aSKzCI6IjIvdng4MuBmIKUQ\nikMkLdMbV3i1y8sr3W6S2dkZ54ykpkZOMK1JHBmYOAzoc5msCKqsb85wYUuGlfVZS6HvnC6e1Il2\nmG9EHtlC8pV9lsYMPbENU5++ns0173zTCDm4ye71nomgqwi7nijZ+RxKg2N8ZwltAwptgy7S2thG\nzEBgKC2zp9dNYhYa4KlUzYqmJaKZ4s/fNSSjW5xPJC2RnUTFUE6DrqhER0SmKyqTUWffdzTX0aJx\nErv3k3j5NdTQQFFjBh58fExZw4lI7zvMwAN/sDNFW1S/4zqq3/UmoPRe7zBCxz5o31Py8zrYxwk7\nzwL64hKdRbaxS2sSB0NuLmjOFNsMZVqYyqkY5LsxVXiKKw+IZax74aou0heXWVhlzQJH0wJdMYVI\namRy1jExnwHeGNCoLnNEOCbD0NYdhH/1O+LbX0LtzxtdKVCG/5L1VL/tGmrfe/24e3TZzuLCzWeS\nPnDU9nytIggCS771ZQRBIDD0akm93lPX0FWEl/+A6YSeZw2O8Z0F9CVkS2HSeFakLyHRMEFzhOnG\nJZswhc2krDR70GzaOqtSnT0xibZB15jdlTRDJJQUGUzJtFbnWOgIcVjGNAza7/x/9N/zW8iN/HHp\n8STRJ7YRfWIbkd8/ydL//uqY7ehMmzrGhs1xdhEVmSXf/grxh35D59ZtpPYfRj8pBiFXBvCuWk7F\nxovxnb/afpMA0Xnczyacb2OGSWQFhiz3zhUIFWF8h/PYp8NDrvVpJWlDOB5W7Knd+7UyrHvQ4OiA\nq2BSl24KHB90IQsmTRWzZ7E0F2j/0r/R/9P7Cx439MQ2jnz8Cyz/yTcRlZG/QSngt3VtaZL6x3YQ\nBIHyd91I+btuRIvGyfX0gSjStHYJQ/OjS6rDaTh7vjNMvqOOdWuRyo391SWyAodCLl7o8PBcu5fn\n2r3sPOHh+KCMNoXP/oZyHZ8ydReQLTQ98Ch2SqtMyixoZR/sochsajDMfGh6eor65geJXXsI/eK3\nRR8ffepZ+n7yq1Gv+y881/K1pfIANe95q+VxpUSuCOA7Zxm+FUtQKsYvV3GYuzjGd4bRbT6QdVMY\n8TA3TTgYUtjV5aU7ppBS852CcrpILCtxPOLmxU4v3dGpyUoWBVhUrSJPsiHDeFT5ijfstWXWQ7x+\nl0GwrLhrRFIiAxZLFZO5/FaBQ3H0//wBzLS1Nm5Dj20d9VrDbe9HtthOLnDZBXiXtloa4+BgFcf4\nzjCSzRCpJJinwqumCQdCLnpirgnFJrK6yNEB95QZ4Dq/zvLaHO4CLQut4lN0GsuLN74NAZ0yi154\nrV8rOlwdSso2+igLDCQd41sMhqoR2/6S5XGJl3aTOEPBytVYly/lKRI5WE3Dx95v+doODlZxjO8M\nE/TrtrzFgOf1MT1xib54cfutuilwPOIiN0X5P/UBnQsXpGmpzOF36flFAiaKaFDth7oylfoyFbFI\n8yVg0lSuWZJxFIW8/GShvsXD1JRptFpIiLIbvrfTe/lsRB+KooUHLY8zcyqp/YdGvb7wK39DzXvf\nVjAZQA7WsPDLn6H88g2Wr+3gYBUn4WqG8SomlV6dcNLKOsik3v+6sQglZKykCw2LSkyVepNLhqU1\nKma1imbkDb4smjTWBwiF8pkjFVGDtkFlwn1TEZOWqhwLbGQKV/kMVtVlOTLgIpkb2+OUBJPasnxH\nJStJWtOR0HU2Y5qm/Q9rjDWdIIos+Y8v41uzgsgjT5LYtXfECkqqrqTiig3UffRmyi9db/PCDg7W\ncIzvLKC5XGMoLRWdwFPl1an25b26eEYgajlbOp/oNdXSiYIAigTKGE/EpgoNr0unJ6aMaj0oCSaV\n3nyf3zq//SSuKp/Bhd4MvTGJUDIvfGGSl6Ws8IxsCmEFl2xvo95tc9zZhlxViVJTRXaCPrdj4lLw\nrRq7M5EgCDT++Qdp+PgHiG55lsQrezFzKlKZj5ob34q7uaEEM3dwKB7H+M4CqnwGi6tVjg240Apk\nPpe7dVbVZU95X7GshGHDTchqArrBiOYI002V16TKmyOrQn9SRjfz+yA1Pr3o1oeFEAVoqtBLWubT\nXK7Rl7AWuhfJ63M7FEZUZAKXXUi2SD3mYQIb1uE/f/WExwiCQOU1V1B5zdndWs9h5nH2fGcJzRUa\nK+uyVHo0hDE8RZdk0BBQObcxg+u0JZNh00YZMGtKX9wKtFRqLKrSWFillczwThUexaTOogJghVcf\n1bjBYXzqPvhuBK/H0pjK6zZN0WwcHEqP4/nOIoJ+naBfJ5ISCSVlFJdCNqvikQ2aKzSUMaLLdkOZ\nsmjOqNc71zlvIUTi+rj7yafjUXSW1TgqCVbwb1hH8Oa30/+T+4o6vvyqy6i/9ZYpnpWDQ+lwjO8s\npMpnUOXLEQy6TyUojUdtWV7cIqVa2/et8BizSht6ruFzi6ypT3Gg300sKzJehlCZS+ec4NgtER0m\npvWfP4dpGITueQjU8WP8FVdfztL//uoodSsHh9mM82ud44gCVPt0UhZqdwVMGgOO1vBk8blM1jdn\n6EtI9CdkElkRw8x/J2Uug9oyjcZy3VKZlMPrCKLI4n/9IpXXbmTg/keJbXsRbTACgFjmJXDJeqqu\nv5rg+25AkJ1HmcPcwvnFzgNaKlUG0xKpIkKgkPeWq3xOp51SIAh5UY+GgI5p5hXLJGF69LTPFqqu\n3UTVtZtQB4fIHO3A1FQ8i1pwNdbN9NQcHGzjGN95gFuGVXVZ9ve7CxrgGp/GOXXZaZrZ2YUggOwY\n3SlDqa5Eqa6c6Wk4OJQEx/jOEwJuk3WNGToiLiIpkbR2uhE2KXcb1JRptFRaU4tycHBwcCg9to3v\nV7/6VV599VUEQeCOO+5g3bp1pZyXgw08MqwI5jBM6I1JqIaAQH7/sdrnJFg5ODg4zBZsGd8XXniB\n9vZ27r33Xo4ePcodd9zBvffeW+q5OdhkWFjCwcHBwWF2YqvS87nnnuPaa68FYOnSpUSjURKJREkn\n5uDg4ODgMF+xZXzD4TBVVa/3yKyuriYUCpVsUg4ODg4ODvOZkiRcmUXoFFZV+ZDl6elnGgwGpuU6\n08F8uheYX/czn+4F5tf9zKd7gfl1P/PpXsD+/dgyvnV1dYTD4VP/39/fTzAYnHBMJJKycynLBIMB\nQqH4tFxrqplP9wLz637m073A/Lqf+XQvML/uZz7dCxS+n4kMs62w8xVXXMFjjz0GwN69e6mrq8Pv\n99s5lYODg4ODw1mHLc/3ggsuYM2aNdxyyy0IgsCXvvSlUs/LwcHBwcFh3mJ7z/ezn/1sKefh4ODg\n4OBw1uA0lXNwcHBwcJhmHOPr4ODg4OAwzTjG18HBwcHBYZpxjK+Dg4ODg8M04xhfBwcHBweHacYx\nvg4ODg4ODtOMY3wdwNDz/zg4ODg4TAsl0XZ2mHsI6ShK6AhyIoSgZQEwZQ9aeT1qcDmmu2yGZ+jg\n4OAwf3GM79mGaeLuehU53IZoaCPf07JImSjKwHHUuhXkGlfPzBwdHBwc5jmO8T3LcHfuQgkfQZjg\nGFHP4erZC6ZJrmnNtM3NwcHB4WzB2fM9i5CGulDCRyc0vMMImLj6DyImB6d8Xg4ODg5nG47xPYtQ\nBjsQKNx7eRjB0FDCR6dwRg4ODg5nJ47xPUsQcmmkeJ/lcVK8H87cG3ZwcHBwmBSO8T1LENJDiHrO\n8jgxl0JQM1MwIwcHB4ezF8f4niUItut4TTCMks7FwcHB4WzHyXY+SzA8AUxEBKwZUlNyYSpu6xc0\nTaShE0ipIcAAUcHwO5nTDg4ODuAY37MG01OO7q9GToQtjdPLakG2YHxNA1fvAaRoF1IqMiKzOhc+\njKcsSK5uOYY/WPA8SvgYUiKUV98SRAxPgFxwOYy3GDA0xOQgopbFUDwYZTUgOMEdBweH2YdjfM8W\nBAGtosmS8TUBrWpB8dcwDTzHnkMZ6hr7fTWLMnQCKREms+B89OqFY5zDROk7iDJ4HCkTG/W2MtCG\nFmggu/ACEPM/XyE9hCt0BCnWj5RLnDpW95SjlTeg1i3HdDmKXQ4ODrMHx/ieRajBZchD3cjJ4gyw\nVt6ANpaBHAd3x0vjG97TELUMnhOvkHZ5R3nA7hOvoISOjFsSJaoZXIPHEXNJ0ks3IUc6cXfvRjwp\nkXk6UiaGlIkhD3WRbbkAvaKx6HtxcHBwmEqcmNzZhCiTWXwpWllNwUO1QD2ZRZcWHbYV0jHkyIni\np6JlcPUfGfGa0n94QsN7OnIihPfwVtxdr45peE9HyiVxd7yEmBgoen4ODg4OU4ljfM8yTJeP9LIr\nydavQvdWjjBzJqD5qsk2riW9dCPIrqLPq4SOjNaKLoAU70PIpU5e3EQebLckAiKlBooun5LUNK7e\nvZbm5+Dg4DBVOGHnsxFJIdd8LrmmtUjRLsRsEgDDU45e3gBCMQKUI5FT1r1KUc+hDBwn17gaaagb\nKWVNytLqLOVYP0r3HvRAXT7cbeM+HRwcHEqBY3zPZgQBvXIBJenkq9tUwTrpLUuJkGVjahUBA0/v\nPszefRjeSrTyRnL1Ky15+A4ODg6lwDG+DqXBrhd5ck9ZMKdPwlIApPQQUnoIOdZDuvUSTF/FtF3f\nwcHBwdnzdSgJhstveYwJ6N6q/H8LUolnVBxSeghv+/MIanpGru/g4HB24hhfh5KgVS2wkCqVR/dV\no1c25f/bH7Q8vlRI6SGUnv0zdHUHB4ezEcf4OpQErXrhKS+2GExAq2w+Fa7WK5vRfdWWrllKYy3H\nevNKWg4ODg7TgGN8HUqDIJJtOR9d8RV1uFa5ALX+nNPGC2jVCzEtpF2ZUukSpaRcAmXweMnO5+Dg\n4DARto3vCy+8wGWXXcZTTz1Vyvk4zGEMf5DM4kvQvFXjeqWGpJCrWUxm8aWjkrTU4HLU2iVFebRa\nWS2ZhRdhyJ5Jz3sYp3Wiw0yiJ1N0f/vHHP7433Lww5/i8Mc+z4lvfJ9cJDrTU3OYAmxlO3d0dPDj\nH/+YCy64oNTzcZjjGP4g6XOuQY50Ikc6EXNJMAxMScEdbCRRthDTExh7sCCQbbkAQ/GiDLYjZeOj\nzy970MobyLZcAJJMRhBwn9iJNCzW4eBwBno8wcDvn0SPJRC9XqretAlXfYHGHtOIqet0/tN/MPjo\nU+Q6e0a8F/n9Fgbv/S2BqzfS+k+fRXQpMzRLh1Jjy/gGg0G++93vcuedd5Z6Pg6zAUNDHmhH1HOY\ngoDuq8EIWHhYCSJadStadeuIl/3BAGZotEEdOVZAbVyNWr8y39UoGcrXEIsShttPrm4FKK97u3pl\nE+myapT+Q8ixXsR0zHLbRDiZeT3eosBhTpJ87QB9P/4V0WeeR+3uO/V619erKb9iA8H3v5OKKy+Z\nwRnmDe+RT95J5OEnxj0m09VH5n9/Ta67l+V3fcOyATZyKtGtO1D7QkgBP+UbN6DUWMuvcCg9toyv\n1+st9TwcZgFCJo6r/yBSrA8plzz1uomI7q/J79MGl02PMpQoodYtQ2VZwUNNxUOueR25prUIagal\ndz/u8FFLlzO8leiVFjo4OcxqBh58jI5//BZq3+gmIlp4kMGHHif61LM0/fVtNH7iQzMwwzwnvvZf\nExre04k+uZ32f/gGi//1i0Udn+nspvd/fkFs6w4yR46fel2pqyWwcQPBW95JxcaL7Ex7SjDSGfp+\n/htSrx3EzGYRvR7KLjyX4C3vRFTmnySFYJrmhFts9913H/fdd9+I126//XY2bdrEF77wBd785jfz\nxje+seCFNE1HlmemltOhMPpgD+rebZAa3cbvdMTGpSjnXokwTsMFU9fQO/ZjRHowNRVBEMDjR2pe\njlQ9PV2FTDVHdvtvIJssfPBJpMXrUFbMngeRg31Cj/+RVz76t+T6C0ueSmU+Vn/zDhbeetM0zGwk\neibLM+vfTupIe9FjPM31bNr1O1xVE4vChLY8y56/+DKpo+OfWy73s/QLf86yz3286OtPBaauc+DO\nf6P3oSfG/Cz8q5fRdNP1LLvzL/LPk3lCQeM7EVaMb6hQuLFEBIOBabvWVDNd9yJkYniPbkPKJgof\nDORql5JdeOGo15Xe/SgDx8Y8j4mIUFVPRqnI7+UaKiBiusvI1S7B9J0sUzIN5MEO5MgJxGw8X/4j\nSei+atTqRRjl9UXNUQ4dwXPiVQSzcPmQ5g+SXnYliMUvDufT7wzm1/20ffivCD/5bNHHe1ctZ+3j\nP0eQptc56PnBPXR+6d8sj2v69MdY8Lk/H/f95J4DHL7t8+Q6ugueS/C4WfgPn6L+I++zPA87nPk7\nMzWNI5+8g8jvt0w8UBQJ3vx2Fn3j72aVAS70dxMMjr+VNf98eQfLuPoOFm14AeTBdnLB5Zje8tfP\nceJVXP2Hxu1KJGBApAcPIxNKiIMy2I7uD5INLsXdsw8pNTiy4EgFKRNHGexEK68ns+iSgnrMWnAZ\nGUzc3XvH7XxkAnqggfTiS0FXcfXuQ8yl8glisoJW0YRe3ug0YJgk/eEsxzvTGIbJgkYvC5pKl6F+\nJomXdzP47E5LY9L7DxO+/xGCN799imY1NqnXDtgbt//whO93/8ePizK8AGYmS+9dv8yHdj1uW/OZ\nDO1f/lZhwwtgGITu/R2uBY00f/q2qZ/YNGDL+D799NPcddddtLW1sXfvXv73f/+XH/3oR6Wem8N0\noKtIsb7Cx52GaGgooSPkFuaz3aWBdlz9hy21AzwdwdCQYz1Isb4Jk6UEDJRYD0LbNtJLrwRp4p+v\nFlyOXt6I0n8YOd53MvPaxJQV9LIatKqFaP463J07keP9iNrIUiMlfAy9rJpccDl69UJb93a2Ypom\nW58b5NkXI+w5mCCdyX+vLkVg1XI/F51fwZuvCiLLpV3YhB94DCNtvWRs6Mnt0258zezEfajtjMt0\ndhN79iVL58se7aD/5w/QcNsttuZjFy2WIPKHp4sfYBgM/PZxGv/yI/NiD9jWHVx11VVcddVVJZ6K\nw0ygDBxHUq2X6cjJMMP+pBJpt5VhfCbFnkNOhHGfeIVs64aCx5puP7mW9eRMA0HLgaljSm6QZAQ1\njffoNuRxWhkKmMjJAaR0jKyWQa1bYel+zlY0zeTbdx1n2/ORUcuxnGry6r44r+6L8/LuKJ/9xGJ8\nvsk9SHXdZNeeGKGBHJFQJWL1ORiihFtLUxfvwq0XNnJadOJch6nArqcpesaPHIR/8SC6jbrg6NYd\n0258+354z4gs9GLIHGwj/KvfUffBd0/RrKaPubd8GC6D0bKYgojuq8QI1DuhQZsIRTajH4WuAiAm\nI8jx0RmlU40U782XIBXwfk8hiJinlShhmniOvzCu4R0x1FBx9ezFcPvRK5pszvjswDRNvvvj4/zx\n+UjBY3ftifP17x3jzr9aZssDjkRVHnqsj1f2xGg/MeztXggXXXDqeRBID7B4YD/ndW5jQbRt3HNN\n934v2JdH9Z27ctz3NJuCHHbHTYbknoO2xiVe2u0Y3+lEyCbye5PxvhH7kyZCPoRY2ex4Jrawu2jJ\nj5PivdPaDnAYKZdCCR1BbTin8MFjjY/1IMX7iz5e1FWUcJtjfAuwc3eMbS8UNrzDvLInzuNbQ1x/\nTZ2l6xw9nuLbdx2no2uMEPNpC/G4t4bdCzZyoP4CLj32GFe0PTrm+VwN0yu60fvDXxJ5xLo6oKu5\ngYY/m8BDlWyKFkrT77wYGXuKckbaXrh+tjEnjK+YCONpf3FMxaN8aDCMlAwjJQcwa6+bgRnOXXRv\nBSbWTbDhyms4C+bkw812EXPFJ4mdiTLQbnmPWkqEEDLx8RW6xkDVDJ784wCv7I2RSumIkkBtlYur\nN1azesX8E/V4+rkBdIv9KZ7fOWTJ+PaHs/zHD4/T2V38wzun+Ni+9G3Ihsolx0fW1RqywvNVF7Pz\n4R7efFUtAf/UzuVdjgAAIABJREFUqkhFntxG51e/g5mxbkSq3nIVUmD89p2KTeUuV12trXGTQXTb\nDbuXTtN9Jpn1xlfIJsY1vCOOA5ShE6h7/wgNjuxlsegVTei+6qLCryPH5Wt2TXEGf0KGfcMvpoes\nj9FV5KETqA2rijr+4c39PPZ0iBM9ox+yzzw/yDnL/Nx6SzOLWoprRjHbSWd09h4qvrZ6mANHknR2\np2lpKk685/6Hey0Z3mF0ycWLrddw3onteLTX+zd3lC/hV8fq4FgPj24Jc96aADe/vYH6utJnZQ89\n8zxHP3mnPcN7/RtZ+KW/nvCY+o+8j/6f/draXqogUH391ZbnM1m8K5Yw9NhWy+N8a+1Fu2Ybs76r\nkdJ3sKDhPR2j9zhisnBxvcNJBMFyKFV3B1CDSwHQqlowSthdyAqmNAkPxWb7QKHIcfc+1MPP7j8x\npuEFUFWT1/bH+b//2caRY/NDlzoa04jFVMvjcqpJ51jh4zFIZ3Re3Wu/HjnureHF1tcNTcRTw5aV\nN576/8Ehlae2D/Iv326jrb2030t0+4u03f4PGAnrCxRXazPLvv+vCPLEi1253E/Fpostndu3diXV\n73yT5TlNlvqPvR/FosftXrqQug/N/f1emO3G19CQ49ay4TA0lPD4iRUOo8k1nINapAE2JBfZpnPh\npMdrunzogeKEL0qJKUhoVS32T2BBUMPquOd3DvHAo72oRdih3v4c//2zdjStlN2JZwZBwHYKgVjk\nk2jLtgH6B2wmCZ6kvSbvOfX5m/jduj+jr2LRqGM6uzN850fHSaYmXmyZpsnAw0/Q9tl/5sgn7+Do\np75E17d+iB4fuSVi6jodX/l3tJBNx0A3ivqQMh3deFYuRWksLowv11Sz4G8/iVDsF1BCXLXVVFxz\nuaUxVW++akbqkaeCWR12lgc7LIk/DCMlpj/7dk6h5fLt80QRU/GCKJFZfBlm+4soQ13jqkLprjKy\nzevQq0ZqIKu1S5AS/Yja9CVCaP4aDL/9fSrDU24pogL5ELta3lDwuC3bB8jmijembe1ptmwP86Y3\nzJ5OO3aorFCoqlAID1rzfj0esejQeymiBEOeGjaf8z52tbwBfYLoyfHODA9v7uPmd469MO2/+wFC\ndz9Icvf+UVsg/Xc/SOUbL2PhP/4Nks/DwAN/IP2avexeACOTxcypCG5XXgP5Z/eT3H0AI51B9HoQ\nZBktFiPx/CvoQ8WVTblamlj4j5+h8uorbM9rsiz6l78l191HbOvzBY+tuuEaWr74f6ZhVtPDrDa+\nomrvYS7oKpgGjKM/fFZimifb/HUgJcInS4wEDJcPPVCHWruE7OJLURMDKOE2pGQYQVcxBQHTVYZe\n0UguuHzM0h69vJ5s47m4u3ePqyZVSgzJhRpcPqlzqNUtyNEuS46a5q99XQZzHLp7M+w9aH3B+NzL\n0TlvfN0ukXNXBXhqu7X8gbUr/DTUFfZm2tpTvPiK9b36M4n7anlp0bVFHfv41jCmCYtbfVx8fsUp\nacOub/6A7u/+ZNy9W7W7l9DdD5Bpa2f5T7/F4MNPTmrOoscNskTHP3+byKNPkT3WafEEIogikteD\ne8lCKq++gvrbbkEpoBE91YgeNyt+8i3a/vrLRB59CjM39sJN9HnyyWTzqKR0VhtfU7T3QZuTiX/N\nR3QNz7HnkGM9Z3wqJlIuiTRwDCXSSS64nFzTWrJl1WBoeeMrKVDE3qoWXILpcufVpBIDo7znYjKq\nDdmDoGUmPM6QXWQb16FXNhec00TolS3oZYeRi8wPMAUJtWZRweNe3h0tGKocixPdaUzTnFW6tXa4\ndlMN21+IkFOL9/wv3VBZ1HG/eKCbZHp6s+sHhzTu/W0voghLF/m4ZH0Fm9K76P7PnxaVNBV/bifH\nPv2PZLt6JzUP7zlLafvLv2fwocftncAwUBrrWPqdrxC4ZP2s+p1pkSHSR9vHNbwARipD/12/RBsc\nYul3vjIjYfJSM6uNr+6twkSwXBJiunzzaoU0KUwDz7FnUWIT//ELhoarbz9StAtME1HNIJgGpiij\n+/Lt9tTaxRNGE/SKZvSKZsREGDnSmY9ACPnmCbrkpizegTEUGvV9GooXrbyebPP5yENdyJEO5ER4\nhAE3JNdJD30pepHNFSa+YYFM60V4255FykwcpjMRydWvQK8qLDFpxeicjqqZaLqJUmK5xelm9YoA\n111Zy++fDBV1/BUXVfLGy2sKHtfelWbfIfulZZPFMOBwW4rDbSle0dJcn1EpNmtg6IltTKJ/DZDf\nW7ZteE+idvXS/7NfU37p7KoGOfY3/0y6SMGNwQf+gHfFEpo/desUz2rqmdXG1wjUoftrkC3u4erl\n09O6bi6g9B0qaHiHEQD5DEMk6DpivA8l3oc8cIzMoosxPeVjn+Akhr+W3Bj7sa5V64gcPYwc7UbQ\nc5iChOkqIxdcBko+7KjVLkarXYyYHECO9Z+Ug5TRqlowXWXF3XSRmJ5y0ks24jmxK1/Da4wWC9E9\n5ai1S4oWcPG47a3I3S4RRZ77q3mAW9+fzwl4/Jkw6jiLEUmEyzdUcfttrYhFRLie2Bo+pQ890+yR\nFyOs+RA37PlpUceb2cltxVRctG5EP97JENv2Irm+EC6b9cClJvHybmI7rDXCiDyyhabbPzLnvd9Z\nbXwRBLSKBfk9ymLHeP3k6ia3HzhvME3kaHHdTYpBTg3ibXuO9LJN+eiCRQRBQK9oPFUjPBFGWQ25\nssIe0WQxPX7SyzYhpIdQQkfzXY1MAyQFPVBf0Ns/k40XV/Hrh3uJxKypfi1tnR+1vgCiKHDbB1u4\nbEMlT24b4LV9cSJRFcOEioDMmpV+rry0motO20MFyKkGjz0V5nBbkmzOQFEEWpo83HBdPdH49Kuo\nTcTB+gu4+Nhm6pKl+/saE5dC4LxziP7wVyU5nRYepP/uB1nwmY+V5HyTpf8XD2FabISR2nOQwYce\no+bdb52iWU0Ps9v4AmrdcqTkAMpQ4QQDQ1JwLd9Q1B7l2YAYDyGVuOZZykRxd+0ms/jSkp53pjG9\nleTG6FFslYpyhXNXB3hmR/ESi4IAGy+ZOJFrLrJmZYA1KwNksjpDUQ3TNKkoV/B5RwZsTdPkngd6\n2PZihJ6+0fuom/84gDJJ6WV/mURLk5v9h0tTu5tTvOxc+Abesv+ekpxv/Aup9P76DyU9pT40PTrO\npq6DKE64v2zLozdNEq/ud4zvlCMIZBZfgtkuoQydGDM0CCfLYJrOxde4FOZJU/BTmAaCmj0ZglVA\nLq7OTUoP2W7zN+F54/0IamZkowKHU7zljUFe3RcjGisu8WrNSj+XXVhc0tFcxOOWaKgb23qapsl3\nftTO09sHx/2lDlgsXRp5bZGrLq/mLW8MosgCd3ztING4PYGVM+mqWlqS8xRCi5S241IhoY7JEH9p\nN6FfPEjipd1oiSSiJOFa2JTPrv7ozcBISdWJkqwmYrKh/NnA7De+AIJIdtHFqMnlKOGjSMkwaCoI\nQl7koaIxv284zzxeI5PAdeJV5FgvYjZxyvjqvmr0ymbUmsX2xSImgahlUEJH0AJ1+XIdXQNBxDi5\nPzoTc5pNrFru509uWsBP7u0inpg4XLq01cunP7ZoVmWfTie/+m3vhIZ3MgjAp25r5dILX48qfOA9\nTfz0V12kSpA1nZWLk8OcbbgWlD4nxsjm8uVCj20dlQWe6+ol8dxOQnc/gPmNLyJffsmp90SfvQW8\n6J2bn/3pzA3jexKjrIps2ckersPZg/P0oSUPHCe3dw/u7MgwmaCrpxKglNBRclUtILsxvBUYZTUj\nPg/DE7DVNKGo+YXbcPUeGNWDVwkdQatoJNd07uw1wmoWV/8hxEwsLzMpihi+6nyuQIkWcFdfUUNF\nQOa3j/Vx4EhyVBZ0daXCutUBPnpzM+WB+bVoLBZNM3n2pdE9f0vFymVlXHLByIjCm94QxKWI3P9w\nL129kxOFkcaJws1m3EsWUveBd5X0nKamceTPv8DQ489MeFz2+Ale+8Tfsejrf0fVm98AQNm5q4g/\nZy3hSnC5qLx2o+35zhbmlPEdwTw1ugBSpBP3iV2neuaOe1wmircnv39jIqL7q9EqmvICFKKEXt6A\n7qtCThW//1j0HLWxkySkbBypP46UjpFeesXsMsCGjrtzF3KsG1E9Y/7RbuRwG1plM7kF55VEoOXC\ndRVcuK6C/YcTbHshQiqtI0lQW+Xi+muCZ63RHWbL9vDYLQFLQEVA5sa3NYwZUbjq8hquuLiKzVvD\nvLw7Rndfht5+62HMivTc05CveMOlJZdn7P7uTwoa3mHUcITOf/kOFVddhuh2UX/bLYTuexjdQj9h\n/4ZzKb988vkZM83cNb7zFdPA3bMXsYDhPRMBAzkRzv8T6yO9+DKQXejljVNifAshx3txt79I1kpi\nlmkixXryNcJaDgQwZQ9qdStGwFq/11EYOp627ROWXUlqCil0GFFNkVl8WckU0lYt97Nq+fht4M5W\nDrfZS35yKcKE9dS11S7+5KYmNpw3vnqTIotcf00d119Th2ma3PG1Qxw4Yq3hwYrwq5aOn2n8F51H\nyx1/WdJzmqbJ0ON/tDQmc+Q4/f/7GxpuuwX3gkaq3nIV4XseKmqsWOYl+P7Seu4zxdwulJqHKOFj\nBUUfCiHH+/Ae2wGmQa7hHLSy6e/VCaBEuxHSxa1opWg33kNP4T26HddgO0qsByXag2vgGL4jz+A9\n9BRiojjhhrFwd+4qut5ZGerC1fWa7Ws5FEdOtbfvurjVxw3XBampHOk7NNa7uP6aWr791fPYdEl1\n0ecTBIGL11uTWVzY5OGtN63GvWRs4RWpIoBcW/wcisG3rrhWlmMR2HgRy+/6OlJZaUvaIo8+nde2\ntsjQ5tc95UVf+wKV111ZcIzg89J0+0epvXFuZzkP43i+M41pIA925BOX1CxikcaqEHK8FyV8DDW4\nlMySy/LyktPccEIwNJTQkYIlPNJgB54Tu8ZtzCCYBnIihHBsB9nWi9CLaG5wCkODXNpyvbMcPUGu\nac2p7k1zmUNHk7z46hDZnIlLEVi13M8F55bPeJKXS7G39o8M5Xh1r87A0Ot7rgLg80gEymQa6jxE\nh6x5se98cz1H21Nsf6GwdnRVhcytH1hA6+o/o+UvPkT/z39DfMcu9GQK0aXgbm2m7k9vIrFrD8c/\n91XM7OQbjnhXLWP1gz+g86vfZfC3T6D2W/tbrr3xrSglXgwApA8csdVXO3tav2HRpbD8rv/HiX/9\nbyKbnyFz+NjIg10K/vVrqfvAO6m96YbJTnnWMPefLHMYKXICd+8+xPTQlCRFSdEu1OBSTMVLetkb\nUEJHkKNdSImBKSlBGnMOBULeQjqGp+vVojoiSWoad8dOUiuvOaWINeY5c2mU0GHkWC9CLg2Gimha\ne0BI2SRK6Chq/UpL42YT214Y5IlnBth/ODEiTCtJfaxYUsbGi6t569W1M2aEVywt44k/Wt837Q+r\nwMhtGRM42p7O/9OR4dO3LcTnK/7xJooCn/7YYnyeDp59cYhkeuxypNYFHj56ywLOW51XeRM9bhpu\nez8Nt71/1LHepa2kD7bR+4NfgM2SmmHye7UeWr/yWbREkoFf/s7S+MHfbyF48zsmNYexMDV7ZVum\nNjJZTZBlWv7udpo//wlCv3iQ9MGjGDkV0euh8rpNVFx5yYwvFkuNY3xnCGmwHU/nrintAiTHQwjZ\nJKa7DEQJtX4lav1KhNQQUiaG0n8IOWWtA83pmBiAgDDB0mG8uuxhlNBhRDVd9DWlXAJX6DC5prVj\nvi+HjuDu2Yc4TkKYFaT0EJN7ZM4cv364h/t/30smO3qRpeuw/3CSQ0eTdHan+fiHJtEXeRJcfUUN\nD2/uL3nS1UuvDPHN/zG446+WIooC2azBI1v6eW1/nHhSRxSgukrhsgur2Hhx1Sl5S0kS+IuPtPLO\nN9fxyJYQB48kTybJCdTVurlkfQVXb6xFtqC/vfDO21GqKgjd8xCZo+227qfq8gtp/puPA2DkVBI7\ndlk+R3zHTtJHO/AuLaxPbgW5amKp2XHHlY+dAyG6FOo/ctNkpjRncIzvDCBkk3lvb4rb7wmmjpBL\n5I3vaZi+SjRfJVplE97Dz+TbB05wHkOUMVy+UbrP2rInEIdakMIT7EUJE2Q7GxpyvG/898dBivVA\n45pRGe9y6AieE7sRzBKVgOilEWOYbrZsG+C+h3sL9hTWDXjs6TAVAZnbP2bvIToZJEngiouq6Ozq\nKXkcZufuGM++FCE8qPLgH/qIjiH3+fzLUR56rI8P3djM+rWv339zo5ePfbB0RqrxL/6E+j+7hf67\nHyD+7Eto8SSioqANxUgfasNIjB8iD1x+IRf+6tvExJPa5wMRcr3Wcx+MRIrUa/tLbnxrb34HPd+/\nG7Xb2t+x/+LzSzqPuYhjfGcApf/Q6FKXKWN8s6r0HUZKRSY0vLrkIr3kckRdRWrbfupYQ05h1B7C\nLAshhs8Z1/s1PIExXwcQk4NIWeudaqR0DEFNj9SXVjO4e/eVzvACSLOoTKpITNPk8a2hgob39eNh\n645BPvbhmVlo3PT2BvpCWZ4qsdCGCfzP/3YST45/XybQ1p7m2z88zl98ZCEXnT91KmOi20XDrTfT\ncOvNI15P7T9C/8/uJ/rM82Q7ukA3kAJllF24jurr30jtze/AXV91SrXP1A1MG3usMDrUWwrkcj8V\nmy4mfG/xYXCpIkD9R95X8rnMNRzjO92Yhi1vzw6G5MLwjp3FqfQfwt27F6HAXqik5/B2vEx6yeXo\nvupTYWq9dTt4o5juGEbtgTG9XxMBtbp13HPbXYAIpp7vNXzaa67+wyVf0OjT0Nih1Lz4SpQjx6yV\n8PT25/jNI91ct2n6JS4FQeAvb22lpsrF9hcjdI+h7VwRkG01VpjI8J7OUEzjrntOsGZlYJTu9FTj\nW7WMRV/7AkYmS64vhJFKowRrxk2OkqsrUaoqUPssJk8qCp5liwFI7TtM+vAxkCR8q5fjHSdju1ga\nPvlh4s/vInv8RFHH17zrzXiXjv9cOFtwjO80I2QSeWWlaUAP1I+tA61rKP2HCxreYaRsHFffAbSq\nFqTUIKacwgie7L8pmujNO8f0fnV/7YSZyYZNbWhTkPIa16fPMT65ZuVnorsDefnOOcbO12LoNhyj\n3fuiM2J8IW+AP/CeJt779gYeezrMobYkuayB4hJY2OSl3C/x/Z8X92C3S18ox8Ob+3nfO2amHano\nceNpXVDwOMnnIXDpBZZ7+/rPX038hVfo/Jdvk3hp9yltZNHvI3DJeqredg3B991gq02fb8USFn/9\nTo597qtkj0/QAEcQaP7AO2j6589ZvsZ8xDG+04xgqFOS2Xwmea9z7BWtK3QEKWetFEOO95FsOg8x\nE0Wo/AF4Xy+JMivbR3m/uqeczMINEyqRGWU16O4AUtZaIwzdW4GpnKbtahqIueKTtgphAlpVy+xS\n5yqSdMZe+NjuuFLiUkTeft3rYioDkRyhgRz7Dk1Po5SXX4ty09vHVsWaTdTe+FYGH9kCavHRgFw4\nQueXvznqdSORIvrkdqJPbif29HMs+fZXEF3WldfKr7iIFT/7Fr3f/znRZ14g1/l6aZ/gUvBvWEfV\n9Vez9vN/RjhsfatpPuIY32nGlD2YiKM0kYseT2GtZhNQaxajVzaP+b5YpNjEiDFqBtfgMbKtK3B5\nj57x5uveL4KM7q8l03IhpqeAqpMooZXXI1nsQqVXNI006qYJJdoxNAG1ehG5xjUlOd90o1jIxD0d\nWZodejuaZrL5mTAv7Bpi/5EE2TGytaeK3r4smayB1zO7F10V12yk+vqri/Z+pYoAuWMdBY8b/O1m\nkCSWfvefbC1AvMsWsfjrf4eeShP+9SNokSiiolB2wVrKL1kPMOsXNtOJY3ynGdPlQy+rQrbYZ1d3\nlZFrWI0c7UGO9iAwtqdiChJqzWKyLevHPZeo2yz617JInh+Aa/TczcoOcosH0ZT3YviDRZ9SDS5H\nHupGUovbp9Q9gXwDhNMRJUzJBUXUCk94bncAraolb3jn6EOisd5eKL+5YebbQw7FVL75vWO8dmBm\nPCNVM8nmZr/xFQSBJf/+ZUxNI/L7LRMe61q0gFyRe7EAg7/bTM2730zVtZtsz0/yean/8I22x58t\n2DK+mqZx55130tHRga7rfP7zn2fDhg2lntv8RBDyessWja9WtRCtdjFa7WLE5ABKuA0p3n9KnMKQ\n3eiBOtSaJRj+QolCNg2LmESQnxznPQOjeSdG6hOWTml6AmQXrMfT+XLB2lzdVUaushVP+0sIahLB\nMDFkBaMsiF5WbTl8bYhyfl9clNH91ag1c78d4vXXBHl8a+ikEEVxeD0i776+kVJFD+yQzRp8/b+O\nse/QzIUk3S6RsmlOuLKL6Hax7Pv/Sv/dDxB55CniL7yKmT659aLI+M9fTeU1m4hu3WHJ+KLpDNz/\n6KSMr0Nx2DK+Dz30EF6vl3vuuYfDhw/zxS9+kfvvv7/Uc5u35OqWI0c6kTLFSUnq7gC5uhWn/t8o\nqyFbVgOmkW9AAJiyq+hGAIbiQbKxRWrW/x5R6hn3fUF+GUF+AlO7ztJ59apmMpKM0rsfOTmAYI70\n6k1RRvdUgKHh6d0zYukgAcT70SUXJoIl5S61ZjG5CSIEcxGvR2L92goee7r4bNi15/hZ3OonZDH8\nX0p+/UjPjBpegOWLfSg2JS9nAkEUqf/wjdR/+EZS+w6RfO0gpqbjXb4I/0XnkT50jK5v/cDyeeM7\nXkZPpkquA+0wElvG9x3veAc33JDX2KyurmZoqLAeqsNpSArpRRfjPbajoLemu/1kWjeMLacoiJg2\nMob18saimwwMo7lymBUvTegzC4KO5PoVmnYto7xr08Q0xzeMenk9enk9YiKEMth+clEhYChudG8V\n7p69E4amJT1nyW/TvZXkGs6xMKJ4BiI5jnWk0TSDxgY3rc3T+xD76C0L6OrNsKeI8O3ihV4+8Sel\nFV6wimGY7Nw9PRUAE3H5RVUzPQXb+FavwLd6xYjX0gePnspqtoI6OIQaHnSM7xRjy/gqyuvZcD/9\n6U9PGeKJqKryIcvTE9IJBscXdpg9BDBqK9CO7MQY7IbcGSFXxY1Y3Yhr6XrKAqUVRDdrziMbaYNk\n8U0czJatiFLh+mRRfpmK6m24pOsx0gn09r0Ygz2Yaobsfih3+xBrmpAWrkF0e0efIBiAxUtev65h\nkNvxEGYRe8LFBtOFQA3ec6+c9Od6+u/MNE22Phtm8zP97NwdIZ7Ie++KIrBmZTlXXFzDe97WjNs1\nPZ7Vt/7pfL76HwfZ8fIg6TF0il2KwHlrK/niX62grtYz6n6mk2d2hGnrKE22elWlQmRoZMhd1g1a\nhlIcqxk/AfD8tRW854ZWJGl27vXb+W60anttLAVBoCZYjm+Kfg9z4/lcPHbvp6Dxve+++7jvvvtG\nvHb77bezadMm7r77bvbu3cv3vve9gheKROz17rRKMBiY0fCZNURo2oBQm0IJH0XQsmCCqbhRa5dQ\n29yQv5dM6e9HCq7Ck3m5qL7Bxul1vYUQdNJD/0m6sxwlfGyUhKaZSaJHQ6gdB8jVr0Stn9j7VEJH\n8cSt6U9r7gCmy4ecCJ8KYZuA4alAK28gV78SMsqkPtfTf2eGYfK9n3WwZdvAqBpbVTV5ZU+UV/ZE\neWpbH5/9xBKqKq2Xctjhr25t4e3X1vDY02HaOlJkswYuRWBBo5erN9Zw7qoAmCqhkDqjfzcHDw0x\nQVDEEvGEissFudN+dud1R2iKpTleXYY5RiJdc4Obv/xIC4ODs7MExu53o7W0IPrLJpSvHAulrpa4\n4CI5Bb+HufV8Lkyh+5nIMBc0vjfddBM33TRa6Pq+++5jy5Yt/Nd//dcIT9jBOqbLR67p3JKdT4yH\nUAaPI+RSCJiYogu9vB61djFCNomQiSMlwpiSG1MvXHc8rGZVNGUHkPQHEfXxNZ9FLYu7azdK+Bi5\nxjX5utoxHoxStKv46w6PyaVILbqYHAJScgBMA9PlQ6tsLnpf3Ao//EUnm58pnEC371CSr//3Mb70\nmWW43dPjAS9e6JvxsPLpmKaJppnIsjAlZSeaBmvPCbD/cBxdz3u9rZEkFVmNlf0xDtSPVHxb1OLh\nK59bTsA//55hntYFBC5dT/SJbZbGlW+8CNE789nv8x1bYefOzk5++ctf8vOf/xy3e/zWbg7Ti5CK\n4Ol6FSkRHqVepURP4Op6BcHQLeU6W/J6hxFNjOadmBNoPkM+TCxl43iO70APHyWz+NKR4hmAaMM7\nFUwdKR5CbTgHo6z0PUxP52h7kqe2F5+5vv9wggce7eWWdzVN4axmF4Zh8uxLQ2x/YZC2jjS5nIEs\nCyxs9nLZhZXU1boQKF2utUuRuPkdjTy8uZ/l+weoyObFKJaF4xysK8cUBNwugeuvDfLhG5vnde1p\n9Q3XEn1yO8WGFgSPm9oPvGuKZ+UANo3vfffdx9DQEB//+MdPvXbXXXfhcrlKNjEHawjJAbzHnkfK\njR86Ew3rKkaWvd6TjKV6NR4CICdCeI5uJ73sShBFlHBb3nO32RrwzIzpqeLxp8Njtu2biJ2vxbj5\nnY3z+qE/TH84y3/c1c7+Q4lRz//woMrO12IsWeihqdFNV8/km84DdPWm+PtPL+HKCyp49v2vi0sE\nUzmu0NJU37CIG66tI1gzuedVNKay+Zkw8YSOKEJdrYtrNtXimkUZ07U3vY3o1ucYfOCxwgcLAnUf\nfg/lF5039RNzsGd8P/OZz/CZz3ym1HNxsIuh4+14eULDa+u0drzeYSbQfB4POTWI99AWBNOw1e3o\ndExx6sOIpmmy54B1z/zI8RSv7I2PaGM3HxmKqfzf/2yjrX3iZKq2jgx+X+kM1mAkx9H2JNn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rMHUToakC8oBdKcYJOr0NJy8eVeFvS+s69wJgGjDaPrBHL3peVHmjkFv70QX9FskKK7X1DXdV5Z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\nGSB9YVknAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sQalLp6NfRcs5x7FER0iluVzL6JGG4nZ+6BLJnbJUxTWF9pBCT0RhTlUy47K3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\nDiNqlSuE0AL1GAH7fZmGp8pSRAXyIXa1qrngcUq0qyhlrQnkdAwlcuZ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WDTZj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\nCEKc/X/yLk4lGzWX2gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HxmCRUsp8+I44iGlXX4qroXZMbDSSGoGXXrc0Ruvqoe32e/E/+8qQNY2IzYdW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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j1NrB2mO/RPf6rQOEtw/R3UPzE39l69euj2nOjUsvG1fCm3nmZ5h8+3dH24wR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\nshxLP7Cz/xqvLWymX+bGa+eydH7iSWu52W6WzM8csSb5UD5zwRR27Q2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573tdhMMGJuD1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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Found in 3081.27 seconds\n",
            "Centroids:\n",
            "[[ 1.65289262 -2.04643427]\n",
            " [-2.0763623   1.61204964]\n",
            " [-2.08862822 -2.07255306]\n",
            " [ 2.09831502  1.55936014]]\n",
            "Cluster assignments: [2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 3 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
            " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nq94r7OhnJ_k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "outputId": "b92f344b-6a0d-4366-a995-0386d6f97331"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import time\n",
        "\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.datasets.samples_generator import make_circles\n",
        "\n",
        "N=210\n",
        "K=2\n",
        "# Maximum number of iterations, if the conditions are not met\n",
        "MAX_ITERS = 1000\n",
        "cut=int(N*0.7)\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "data, features = make_circles(n_samples=N, shuffle=True, noise= 0.12, factor=0.4)\n",
        "tr_data, tr_features= data[:cut], features[:cut]\n",
        "te_data,te_features=data[cut:], features[cut:]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(tr_data.transpose()[0], tr_data.transpose()[1], marker = 'o', s = 100, c = tr_features, cmap=plt.cm.coolwarm )\n",
        "plt.plot()\n",
        "\n",
        "points=tf.Variable(data)\n",
        "cluster_assignments = tf.Variable(tf.zeros([N], dtype=tf.int64))\n",
        "\n",
        "sess = tf.Session()\n",
        "sess.run(tf.initialize_all_variables())\n",
        "\n",
        "test=[]\n",
        "\n",
        "for i, j in zip(te_data, te_features):\n",
        "    distances = tf.reduce_sum(tf.square(tf.subtract(i , tr_data)),reduction_indices=1)\n",
        "    neighbor = tf.math.argmin(distances,0)\n",
        "    test.append(tr_features[sess.run(neighbor)])\n",
        "print(test)\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(te_data.transpose()[0], te_data.transpose()[1], marker = 'o', s = 100,  \n",
        " c = test, cmap=plt.cm.coolwarm )\n",
        "plt.plot()\n",
        "\n",
        "end = time.time()\n",
        "print (\"Found in %.2f seconds\" % (end-start))\n",
        "print (\"Cluster assignments:\", test)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0]\n",
            "Found in 2.28 seconds\n",
            "Cluster assignments: [0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2A1v3qHHzxCQMGXitXvZdM8X4dasKldWWoPtgMLmw97AOTz7cg/F7Siv8D6XX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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pg8PhYPHixejfv//FYxMnTkRaWhp4/1sM+tprryE1le7LRRpfBfTb246QYOPy/LtTx/E2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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6S4Zuqv-nMkK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3fGmsml4LOt_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ynluUE93LOi0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}