secondary_work_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets, svm, metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits = datasets.load_digits()\n",
    "type(digits)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e78efa15f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGgAAAB4CAYAAAAeyrc6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7901b0470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images_and_labels = list(zip(digits.images, digits.target))\n",
    "for index, (image, label) in enumerate(images_and_labels[:4]):\n",
    "    plt.subplot(2, 4, index + 1)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.title('Training: %i' % label)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_samples = len(digits.images)\n",
    "data = digits.images.reshape((n_samples, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a classifier: a support vector classifier\n",
    "classifier = svm.SVC(gamma=0.001)\n",
    "\n",
    "# We learn the digits on the first half of the digits\n",
    "classifier.fit(data[:n_samples // 2], digits.target[:n_samples // 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.99      0.99        88\n",
      "          1       0.99      0.97      0.98        91\n",
      "          2       0.99      0.99      0.99        86\n",
      "          3       0.98      0.87      0.92        91\n",
      "          4       0.99      0.96      0.97        92\n",
      "          5       0.95      0.97      0.96        91\n",
      "          6       0.99      0.99      0.99        91\n",
      "          7       0.96      0.99      0.97        89\n",
      "          8       0.94      1.00      0.97        88\n",
      "          9       0.93      0.98      0.95        92\n",
      "\n",
      "avg / total       0.97      0.97      0.97       899\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Now predict the value of the digit on the second half:\n",
    "expected = digits.target[n_samples // 2:]\n",
    "predicted = classifier.predict(data[n_samples // 2:])\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (classifier, metrics.classification_report(expected, predicted)))\n",
    "#print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(expected, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAB4CAYAAADSWhi9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPw2R45dGOqE6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7901d4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))\n",
    "for index, (image, prediction) in enumerate(images_and_predictions[:4]):\n",
    "    plt.subplot(2, 4, index + 5)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.title('Prediction: %i' % prediction)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[87,  0,  0,  0,  1,  0,  0,  0,  0,  0],\n",
       "       [ 0, 88,  1,  0,  0,  0,  0,  0,  1,  1],\n",
       "       [ 0,  0, 85,  1,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 79,  0,  3,  0,  4,  5,  0],\n",
       "       [ 0,  0,  0,  0, 88,  0,  0,  0,  0,  4],\n",
       "       [ 0,  0,  0,  0,  0, 88,  1,  0,  0,  2],\n",
       "       [ 0,  1,  0,  0,  0,  0, 90,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  1,  0, 88,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0, 88,  0],\n",
       "       [ 0,  0,  0,  1,  0,  1,  0,  0,  0, 90]], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = metrics.confusion_matrix(expected, predicted)\n",
    "res\n",
    "#type(expected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9688542825361512"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(expected, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        'p', 'r', 'e', 'c', 'i', 's', 'i', 'o', 'n', ' ', ' ', ' ', ' ',\n",
       "        'r', 'e', 'c', 'a', 'l', 'l', ' ', ' ', 'f', '1', '-', 's', 'c',\n",
       "        'o', 'r', 'e', ' ', ' ', ' ', 's', 'u', 'p', 'p', 'o', 'r', 't',\n",
       "        '\\n', '\\n', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        '0', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1', '.', '0', '0', ' ',\n",
       "        ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '9', ' ', ' ', ' ', ' ',\n",
       "        ' ', ' ', '0', '.', '9', '9', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', '8', '8', '\\n', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', '1', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '9',\n",
       "        ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '7', ' ', ' ', ' ',\n",
       "        ' ', ' ', ' ', '0', '.', '9', '8', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', ' ', '9', '1', '\\n', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', ' ', '2', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9',\n",
       "        '9', ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '9', ' ', ' ',\n",
       "        ' ', ' ', ' ', ' ', '0', '.', '9', '9', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', ' ', ' ', '8', '6', '\\n', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', ' ', ' ', '3', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '0', '.',\n",
       "        '9', '8', ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '8', '7', ' ',\n",
       "        ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '2', ' ', ' ', ' ', ' ',\n",
       "        ' ', ' ', ' ', ' ', '9', '1', '\\n', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', ' ', ' ', ' ', '4', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '0',\n",
       "        '.', '9', '9', ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '6',\n",
       "        ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '7', ' ', ' ', ' ',\n",
       "        ' ', ' ', ' ', ' ', ' ', '9', '2', '\\n'],\n",
       "       [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '5', ' ', ' ',\n",
       "        ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '5', ' ', ' ', ' ', ' ',\n",
       "        ' ', ' ', '0', '.', '9', '7', ' ', ' ', ' ', ' ', ' ', ' ', '0',\n",
       "        '.', '9', '6', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '9', '1',\n",
       "        '\\n', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '6', ' ',\n",
       "        ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '9', ' ', ' ', ' ',\n",
       "        ' ', ' ', ' ', '0', '.', '9', '9', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        '0', '.', '9', '9', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '9',\n",
       "        '1', '\\n', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '7',\n",
       "        ' ', ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '6', ' ', ' ',\n",
       "        ' ', ' ', ' ', ' ', '0', '.', '9', '9', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', '0', '.', '9', '7', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        '8', '9', '\\n', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        '8', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '4', ' ',\n",
       "        ' ', ' ', ' ', ' ', ' ', '1', '.', '0', '0', ' ', ' ', ' ', ' ',\n",
       "        ' ', ' ', '0', '.', '9', '7', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', '8', '8', '\\n', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', '9', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '3',\n",
       "        ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '8', ' ', ' ', ' ',\n",
       "        ' ', ' ', ' ', '0', '.', '9', '5', ' ', ' ', ' ', ' ', ' ', ' ',\n",
       "        ' ', ' ', '9', '2', '\\n', '\\n', 'a', 'v', 'g', ' ', '/', ' ',\n",
       "        't', 'o', 't', 'a', 'l', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '0',\n",
       "        '.', '9', '7', ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '7',\n",
       "        ' ', ' ', ' ', ' ', ' ', ' ', '0', '.', '9', '7', ' ', ' ', ' ',\n",
       "        ' ', ' ', ' ', ' ', '8', '9', '9', '\\n']], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "import pandas as pd\n",
    "h=list(classification_report(expected, predicted))\n",
    "k=pd.DataFrame(h)\n",
    "k.values.reshape(2, 319)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(expected)\n",
    "df2 = pd.DataFrame(expected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e791099a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(expected,bins=20,kde=False)\n",
    "sns.distplot(predicted,bins=20,kde=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1['pred'] = df2[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  pred\n",
       "0  8     8\n",
       "1  8     8\n",
       "2  4     4\n",
       "3  9     9\n",
       "4  0     0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_numpy = df1.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8, 8],\n",
       "       [8, 8],\n",
       "       [4, 4],\n",
       "       ...,\n",
       "       [8, 8],\n",
       "       [9, 9],\n",
       "       [8, 8]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pyq7VNjFT/sy/rwAXZKXX84oGfhAwuWhgZZ31wj15mpmZ0ZDfr6uYmAvDrmX0BbYDvhER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\nZP4/4nqdQ9KXAYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7913ae5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "heat_map = sns.heatmap(df1_numpy)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "################################compare and then predict an image#######################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [248, 248, 248],\n",
       "        [255, 255, 255],\n",
       "        [249, 249, 249]],\n",
       "\n",
       "       [[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [244, 244, 244],\n",
       "        [255, 255, 255],\n",
       "        [252, 252, 252]],\n",
       "\n",
       "       [[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [240, 240, 240],\n",
       "        [253, 253, 253],\n",
       "        [254, 254, 254]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [253, 253, 253],\n",
       "        [226, 226, 226],\n",
       "        [255, 255, 255]],\n",
       "\n",
       "       [[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [253, 253, 253],\n",
       "        [226, 226, 226],\n",
       "        [255, 255, 255]],\n",
       "\n",
       "       [[255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        ...,\n",
       "        [253, 253, 253],\n",
       "        [226, 226, 226],\n",
       "        [255, 255, 255]]], dtype=uint8)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "img2 = mpimg.imread('11.JPG')\n",
    "img2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Aoim9wVzYt/cOdrZtNL/b4euvfRO3XmHCgOdOTBCsHDa70AOmR8Yyx2miFb41WeIgJNYq\n72XqOiSWVk9zpK8jzWGQ+ojTedAjHRuEyl7nn+XfS82YfhRSr/rElrBJRwqnYrwUSRwRJwOalibx\nMZMzXP+BjwIQepqfee3fMDphNOybD97Ozm3nAHnzLzclZ06FRybdcodq0de61bCZKMyXuPu5efHx\n7rdEiRInD8pM1ONEl4Rx7WXLhE4ionpesNYY9hlYIuOdO2dg3xwAX3vF67jpvR/kYdMmCqO8hLm5\nfQDsGp8iCuOsx62QTqZWa0z7wXShjOM4E9+ijLLcJ8i0JagGmY6vzjN6ARQSbJ2M4zkMdIL07H3R\nEOlcL3AcidLGO1qJIx7WNhGam973Cb7s1Ljof/05ALtmJrllbS8Ak+3tNBNJO1Vz0l68bes0d8h5\nV+XWfUy37pltcbTxWLljf5ZhKuoeHdvuuoWDXFpmdMT2Ztk3x0df8w8AHPrEpzm3NYpr2wiuJj3G\nxwy7VTVKSLSTqc5Ssy7bNJ3kwFH7td4dSvmyHpl5J/I2lw6ANtE1A22ECIBUJKw3C4sppOsCwb7G\nXTULx+PHZ7nx/VdyVc+E5Z/6hr9hz4jp5XMoXGLEn8pvjgdUJDotxANqtiDS28JGaClAjhNuz9S1\nRG7aZ1UxZidD1FnCr7twxw8BuP7N/8a+D1wJQC0OkadtpztnNJJ2opkYM4Jmrn8Y3/PzVXDDvCmm\npW/ctjEXJNVcyiSyI6Ft2z5V6Her2Ti+Am0lRpJ+eox6mRRBOGRq3NzPYHmN7Uryg499FoBPtV7N\nU/72fwLg1X2WO3OommGca1VqoCGxvCK+K7ObqMMIUdmadAxb07VbokSJkwKlBnK86GmoCnrCmCJN\nINxnKjKrjRq4kstf8TcArF35Zc6rmuxEsW2UVYY4NkGsrlyirrF1leOTiPXhxCKkLkT57sEuyYrE\ntq72+4BhXVKdSDXI9eNrwuJWA+Dux1GL9VpItVphbtUkCY41x5iptFBdk/Z+87s/ykcPm6SyX3jt\ny3FmR1nEbOsGfcZEnaosZKqmp+hvTe0DSgFy/Ggaj1t1YB7+xcU5ZsYsK9Whw1z3xrew70qjup7b\nGqVZNUPdXV1F6JhGownAChG6a3I7xtsjrOjhusNsNE2Olra+EWX5/rERO7lhtzHcLXWa/EFmshSr\ndzdCaJUJH4AwjBB1E9I9HHepo9hhQ/jB0hr7PvoZAK6dneYxL/tj2pYdbaHfI0BTraasVA5xZHxq\nolXdstnspQlTokSJ40apgRwn5uswnUB1xWgM1UoDVgxd3TVvejM3vuNSHjJqnGmi6bIUGM/8SAQT\nwmclMKZPp+pQlcaRFvUUcXO9ry7NlHTU+s83Ou+K3Bbpe8hXT6f0pmYIrf0ntcg1OgWOktmKqiHr\nsyNslurRNLtcczHvV6Mh0zu2AXDX3v2AZDA0ZZbTtRozo0ZLve7SD9Ij5KI/+E0Adp9zNkE/JLId\nBL3mCK5vqvBOyUSyBzsC4M6VRXZXbZ11X/Hpv3kDAN+67DIu2rObuGMESmd5mZplSW812ogQsKG9\nmmgi7USZ7/dwZT4Zi0IizTRdH0osbGe9ip2p3ffHxZ5iSGlHnUJcXG4QDo6WqPQ+CNBasr5OVGXf\nW0faNNbijkOHANiz8zQ6cwssdExzsNOmd7C4ZNLcz6g2+eZbL6XtGJ/Hjz3/t6lsn+ZAutA4Daq2\nJjfphrjNdfW5WwalADlOLAYdpibG0QPjBPvMy9/Avss/B8Ajp3ayb22Okbq56RO6Ttty1S31VwmU\nYLxmtBPV0xyWxgcSbh/D6czneSAc6TTNhEvhXO5JSJSO1PVIslgtiA057EXhnQoVpSWIjdXOuXbi\nFPJCVpKACesLW5o/TNutIEcMH8hdywepWWf6qOty4fRpfPXt7wOg6zr8xEv/mMq4SUI7OFxjV9Xk\njFQqW1N4QOkDKVGixCZwSmsg2maGHq39QhqiK7ZfWFUhY7KKu1Yg0ajDgrVS1oCVgWEEe9T8Ppib\n51v/8HoAvnvFVZy363QAmlITLsRMusa3oRIn4071ZQPHga7NgU/q4KUaR2eeyjEM3kTc+6ra0udx\n92gPj66SJTbj1ODeD2Dxlp3ea6P79nPZZCAUSRoqbtYJrF0Uqy5hkPConaby+htv+BfC793MxW/4\newAmpsZY3Gu4ccfO2IUMp1D2lHQ1P56IE6QUIM2ZrxCirenjAi0loC/TC8yYzTrV+6ddxCktQERg\nB+4o7OlpynCRPX1KVOntm8edsWxhEhhGTNpvhEGXnW0bql28ge/+81v46pe/AsC2mVl0Yu7wWneN\nVqtFbNPV7yXH0r0K0ZbY2tBIFHmm67HIruu1CqsLJmdkz/YdfP+666n8w+sAeMJf/BkTs6ZMIlpY\noT8+Rc0KDhmCUFbUeQ6IhH5iEkeqjp+Ztz4CtDSUmubksie+wv3D9l6aMCVKlDhunNIaSKZbHqX9\nQr4wFNovhNCYnCaxuTx9FJ4Mqa4a6b7d9+gtGu6OW//5X/jCpz9jqPmB2ckpli1pkBdraqNtOovG\n417z1jvBiquS0AUNpdRATnqoQiaxzv7ZKt4NbKwiUYy3TELhfK/PdKXOtZ80iWY9lfC0F/8pAF6z\nwg96c+xqGZ2hJV1Qdu2PEqgKXMeoJw7gpHaYAqQkttMvJGdJrCS2XUTBHDoenNoCJH1uj8Kenlo0\n69jThYaKQFvJU0OztDRP1ZZnM4j4wsteC8DtH/wI46NjTFiveTQY4lqGb8+TBEGAl5LOFPhKU+FR\nZMVKj19GS05+aJGnyishM7b3dFtauyu0pOq6rC0Zn9rM+Dgjvkd/1YR8v/3xT+GEZuH66Rf9Mdsf\nci6DVdt03WuAa1PedYJSLp4lnhIJR2V7ByNAGunnCSYXoRQgd4+0V+3R2NMzAuQCe3pYE6wyYNR2\njZMLi0xPToDVOq5+xes5/JHPA7BzfIparcbATgCihJG2dZoGEb1BnzFbzj/s9Y8pHDLfR9lF7qSH\nnTn23bEZ33UUU7MMc6rbQ3g+ZzSMj02urXDbZ64BYIdb5/x//GsiO096YQ/LrojnVyDWCJuYiHBy\nTdbN2d7BrKf3N9t76QMpUaLEceOU1kCyasqjtF/ILrzQfkFiyIDirlEVa60GLK7wxb8znvEv/993\n8uM7zgBgpDlKp9PBjWyrQtcjGRp1RgiBW/FZ6dm0ZOkckUGano/gyIzTEqcuivc3SSJatofM4fkF\nRqanMy1jWjp4ddMi85rLPsAPlOIZL34hAN5Z29jbM/61MW+aputD15bvVpxcs3DzdhFgW0ZkmYkK\n6orNioD7oy+MA1wL7NdaP10IcTrwXgwZ+XXAr2utw2Pt40QhLYI+Knu6dYKsZ09XJPvmqKZ6XhRy\nzcteyXffdRkATz7rHA71jY3qLAg816XVMGZLrBJ6Q5NRWqlUkK5Lr28SApx6PZMYWq/P0djYz6XE\nyY1iXYzWR2d7B5OS0WzWObD/AAC7d+6mNxgSWBb+HZNjHFwwc217rca33/4fnL/d1Nic/Zxfwp8y\nztdOvIrvTuA37Ww3RTzmGAJ8Te4TUWQ0jdpTdNG0Nnm994cG8kLgRqBt3/898Dqt9XuFEP8CPBd4\ny/1wnPuMqNCo+Aj29KOs9AcP7GXb2CTsNxylV/3t3/HDj17Fw7aZGyeGHVzLfdmQDaTj0rfdyjTQ\napnVJExiBoMB49bBOhzmJfpKmKjL0fg8SuXj5IfQ5Bwjer12uZHxfbnXYXK7SSSbW5xHCAffJnv0\n11YZbxndIYwSnnLWeVz1+jcDcHBtnif+5Z8A0HMT5rqHGB/dnh0iXf/ctFVEGpXJ11HCLFSwOWy2\nsdRO4OeBf7XvBfBk4D/tV94J/NJmjlGiRImti81qIK8H/hwyTWgCWNE6o6zeh2m4/YBgzWoLk1TA\nsn7hu4S1PFuw1u+zasNj27ZNwtwS17/hbQAc+M+rOG9qDOpGzi4dnue0tiFADhPQUYzvGtVRC4gC\nyywmwXVdBoHVPDawVkHuKC+1jlMLEpVV8RrS5vWV0UWiZsd3WYvMHJGNCmgIRW7fhqEpxdAC6Hd5\n5Jgprvv+Oz8IPTO3n/i//hS2TXB7x2jNU60djNpZ5SYYZrOCTyS2qm9IRIXNM51tpjPd04F5rfU3\nhRBPSj8+ylePauH/KHrjjgrj6OjunaM5NWNOxtes6D41e5e9xSVmx21S72138fE3vZnDVxgmsdNb\no3hKZb6NsdFRvL4REqEwqmZq7xZp8lI29btrt6AF2ahocfSOciVOThSreLVWGds7pE59bV/LjO09\n+90x2N5lFdS8cZxeODHDXVeZEorP8Y888RUvYsc2U36xd3UvrRH7PMUaXJEJkECFROnC6fhZv+bN\nYDMmzH8BflEIcQfGafpkjEYyKoRIBdNO4MDRflz2xi1R4uTHZjrT/QXwFwBWA/lTrfUlQoj3A7+C\nESrP4QHsjeuuGgnbnJ3J0tMTIlrDGIYmQlJteLBgEsWuedUbuePjV9Kykllun6E7N0c7NEvK+OgI\n+4ZGHtass+tui6Xu4dyKvV/SLyelPXPSQwu1Lts4bRcBlt/F1i1oofN2EemXC9iovQ6CLuPjJvIS\nLC0xYtfoWz54JX0JP/fqvwKgPjLK4RUzR8Nqi7FKK9OA4n5Eo161hxOmkdUmqUZORB7Ii4H3CiFe\nCVyPacD9wCBK7U1T1wJQQ5DMH6Y2Yt02yZDP/+3fAXDLpR/ivNYozqzZtqIHuJ6kblNao24InrkB\nSaHAcSPWMVTdA9QG06fEyY2NjO/FsK6imKMq7hPbO0KxatnK2mNtRGjm845BwMJHP8vnrDXypL/+\nc9pN81gPvIBlZEZm1ajWcjo2CXibJyq6XwSI1vpzwOfs69uBH78/9rtppK7dMMEXxiG1urLAxMwU\n7De0c595+au446rPAbB7YoR2zWdt2diaqIh6s8VKlkMcMGULmhZV/4jDFW/4PbWbPNrEOZbfpMTJ\ngY2M78Xm5lLLdd7z4uKxcT6kbO/md8ahWrelEh0VZq6Ss2amaM6vcPv7PgbA1XHIxa96MQCVdp3l\nYQcZmsd8pDqeHT/s93FG6pvOZi9T2UuUKHHcOKVT2ZdttulY5FA5uAZApd2EYZ+vvMkk5dzw75fx\n6F27AZAtn5VBl0ZkotDj+KwGCZ2aGaa60ybuWUYpmx+cagyCXBpvXHlgvalTXHmKDZ3BphuXOGkR\nOhqpC/yqlu0dzPxIb6+DzNje4eitSosaSFT3cdqmAnfvnXcxWTNFd1E8pK4izmsYdfv6D12BP2Ze\n/8T/+B227d5Jx2ZEh/1V/KZJQ/Cb9QL72vHjlBYgXfu311thp5dSkEV84TWv4dr3vBeAxz/0oRl7\nem9l1aanm4xSGQmcYUjNtTXPrs8hy1eXhuoyFbDw4Kc3/2hZiFkXgSJnRGESuSUV4UmNWFq2dwAl\njsr2DqB0zvYORda69WzvYAXPTIvb9u4FYNvUJOGymd1z/TnDRbNsUuAfOTnLtZaoebzS4iHP+e9U\nZozZvUxIy65QFQQyIGfqO06UJkyJEiWOG6e0BnLn4kEAzpnYRieaA+BTf/FKDl9+JReMmcSbw8EK\njmtMlhlZo54IFvqG4yMWLhP1MeKuWRXm6DGcNapjrW+4LNOF45561W7oHrAOZQXuqYOk0IvDtIwo\ncN8V+/qIvF1Eug2O1i4CQHGgs8zEtKmtCpb7jNSNDe37FRY6a7gNo0o4g4BHtE1e1Wf/6R3cubLK\nT7/izwCoTs2yv296806JNqPO5glBtowAOdbzt86XYNVDIazzIFUXC8zFURgyHA55QsNkkHLrN7j1\nTe8EIHj/f/LY2hhT2pgpdx5cxZk2QmFRxsw74LrmZvgJdJIYYaNdLQmNFXMDTEzfkOhiX6cRslCu\nV+ykBqlT1RQcrajYyI6jFY7Oi68c5RJavTdwXJSN90slqEfQsFXbfpIQWzakTiVhtQq9tK2qkFQi\nMzkaoUstkvSORfe+SXR889SkE17YawRTeexoEOTXuFHxTcewmJULkpoaZO8U60PnScb2lcLSBmqJ\n1BJhTQNHFUwBLRAagorxh/U86Hv5ubQCyejA7KcSSwL7dKxVJAMvv4fNEDxLkR45mpUqdO0c2bXs\nHVF9m4Z248JlqwJTmTm3fOzSyylas3tWd6CluflKOqjYZkRHAT51QmuLrPk+2tL8zw4l3oc+SDxv\nFtKRP3khI+cZOoq7okUWZs84nT9/AAAgAElEQVTiLLv/YsHnfcGWESAnBLeZhJqPvPb1HLzm2wCc\nNjtFFAkWE2Mz+s06vb6ZqF7VwS2EUtMHQlmBkIhCU6IMKV2dvNvEYCXIpaBWaK2zSeUUFilhS/3T\n9JXAyV971uMl01RoqbJziSUooQqrmcwSoSXm88EJdK5UUjIsfeQxMu2qcI0pZGGFzT7LVmCVXXuK\njLhtw77WPbBAIvIQaOIUGmajEbpIOVj4rZaGRd1+VcnifhWyQMMgtTJ1Jun1x/l9Gnr5taQC8Wgd\nA4+GdY2rNqyobqxIZKrasL79hFQ5D6uAMLY1YJOTuMMuX/jCFwDYpWIu+LM/Mq8vupDrdQeEcbjW\n63UOHzYJlUrd+7lS+kBKlChx3DilNZB9/26y6G+68os0rClQ2TbOoUPzjPgmJDYx0SJYNFEYL1br\nTKlEQiJl1hhIF1YsqQu6JuAoZXpwkKvrdwctQNvVpFh9Ceb4YbqauYpYpqaAJHKM5gFQi1W2TIXO\nxpUtz3lUIkYJmXFUnAhUo3x8wFadFlZEQy5sL6pwnsKu7OvZ2vJVNpLyiGxM8z25fuXTuQ6jhUIh\ns/E155K+Nr+qW9WteOwUKtNOVPb99LtOwQz17P2XidFA8gSyjRlhBeWTe4YovChqJKGTTS+QaR4r\nVp3SJPZClFDodF5EMRXHQcVGX7nms58neOTDAPixx13IYGWN/QNjzu3YscO4BYA4vvfm7iktQA5c\n9TUALqhP4bSMjdiPhwxrDhVrn6wNuzR96zwIzcBFKaMTkMgkq18oEsT4SZqybn0yWhghQmqHrz+X\nVP3Uwqi7KpsMhQku1jNYJYJMgKR/M0NJqOx1LFW2DzAPQW7eSHATWieQEy4zXTKTQqJ0WvOxXqAU\nvoaSqXlnPkk2CF4t5DrhInThgd4gD3PTDhCKOBvD3NRL/SbjQytcKQoeE0LP2MRELqz1EVmjOvud\nEuaepCZNK9RHZJgWT/VY5NobSyCKwq3nKnR6j2V+79Ps1nyRU0jXTOB+Z5W6K9m9YycAy/Nz3Pa9\nHwDwY6s91GqPT33+ywBccskleJap2XXvvVgoTZgSJUocN05pDWSn5exQuMwt29Bs3WNkYhRtqQiX\n5hdop31ftFq3eoeOcVCmTjhXrU/0khQcrip3ujpq/epR5P9IrGJcXMFytRlaYfEYiqH9ZmIjfum5\nRY7OjmdWNZlrAgUHa+QqEg3t4YlbKwIbBk/HSWjwstfrl1xduIZEQyx1pp3FUuWrPlCN8iynYljT\n0Xodl2wxozMhDZ9aSAoEP0bHadjq6uL9jCW4ShdM04K2kO674LROIzQmypJHhLyCA/KIpMF7CNdv\n5Mctfn3NIzdDxUYzWWXbtFCZCdJs1om7XQZ9M9drjocIraoUa/TakJtuusm8LZgtUt77uXJKC5Bm\n1fg5gk4P4ZgB8msuSadLzfatbdaraOu11jIhcCBwzQCaSSJx49TvILOIw7JNTk0fEEfmKvZG2zr1\nA4AxVyJHZyaMieykYUzFyEBkk9NMcLOjyDGTJp3wkjwqoIWpyk4fYONFX6/jDzZPPnX3EIk5I/vw\nOIBUaRh1/UOhCra9sOeeFMwyVbDlvULIqyhA0jDx0YoXhdYImT9grgJP5kIYoJIYwSQ1mVkQS3Mv\nUwFiBHvBt1MoOYgK91pviMz1N0Rh4EhBcjRsLH04MlqlC+8LIXNriInC2KTuJtd1SeKQ2M6hkXqL\nOKXqCTQVJTJzpVarsWD79N6XKMwpLUAWAuMgwpeM1EzeRxLHBL0edZuI06g2SCJTqRsCfS+fBLGU\neAl49mGoJFCzq9diDePVKvgr1luEMrdZN0CL3OG6rhUh4BcykZJC27qg4MADcBOZCxPbKTxdyTfm\nGCA0Xe/EaSCN+O6dkXkENT++zpya5nduvrBmr7XIhVC2z/SFXfVlYUueP6GQWq8bUxHn28A4Z8E6\nn7PzNeHuVAFJZIHXQysclR+vmFsitMDX4NinduCpu/V53KMAOYYPRKrcGysLwtRVIJXKnMaR1Ggr\nbMJgQNXzqKYUFJ5PxzpUCWOqOPh+XtKfZM3g70FVKp73vf5miRIlSmzAKa2BdCtGkqq6TzIwyWK+\nVkw22ujIhCVWO8tUbfViX2gGrsgyBk1ilzTSHxAqt3UrkbQRBptkJs2qCMXsw9wnkEpqV5nMU1el\nKrYoqOaSwJGFjMskU2tdZVbuYiQi3atUDo6GSpymJitrVpAtv5E4cWuFHzvrsnJjIbFKEcmGZKri\nqurYldQtaHhptqfAoS/z6ZmInM0rkWSmTr4vlf31E4Vn75mfaFKrwlNGmzhsSbI3hpsRuS9BFMyb\nNHs4rap1FTjpnNDSaldmW39DjevGCM7dYaMGstE8G0qJSM1CZHZNfmJOOfXzuFIRWJO8XpGMtBp0\nl0z29NzKGrWzzjZfbDTQS4fp94/Ca3MfNJCTToCsS2W/BwRV80B14gEt13y/rjzCbh/H+gScZo01\n2/cqciVhElORRuWL+zG+I/B8Y+6sBgOqtuvUIIxwfZ80Oro6GBCkKmbVRzsyu5FjzTaDNePEdYYx\n07UGTc/Y4Wq1gw6MCdWuN/mhGlCrmJNrjbZZW5o3O/U01UadtY7JoK1XWygbb1bCxUtEwRRIcr1d\nCEDRDDfPPnV30BUXtKaXsoh7AmVDidpziBHZWLhCogNzbu1qFU8JwlVjalYdn6ZvxmXQ67PSFEg7\nFoFOWLU0lIFSyKqPsin/SRwjEus4lA5jFZd6ZLbVkphRy5zvKQj6AzrbjG9s8fBhxtuWdUopkjim\nYv1fSTBkrG1KHIYrHapeFUeaa+p2u4yNmnqTsBdR9asoSwHRjPInf2Pm7EZImXt4lVrPXmZSBswb\nrTWrTkSrac572FnFTm3GGyOE/YTumpkXI9tmSazPTquY3nCYmSnNqp9bkpG9H4WQ7X0RHNk13Odf\nlChRooTFSaeB3BekpkgxjJrWO8T2yvsSBo7NwEti3ETS8oykl45GaYflwEjrpN3gm/sNJ0Nr1w5O\n27OHPeefA8D0nt2MbDcVvmJyDCoVsKGxvT+4mZQ8rn9wnhs+fw3fv/47AOyoj7Jtm1nN7lhcZurM\nsziw9w4ADt51F2efcRoAK6sLLC4vUR01Iec7l1eIlFlZhfJxlFNQ4xMQtvBKxCBifKsBnAis9EKk\nlGi7fDbcBtKaaGoYUq/XaTYNIXAShNStQ3t1cYm1YcIO23IjDiNu3meoJuM4Rl9wJjM2CWp2z2nU\nJw0ZjhxpIeuVrEXBcNhn8aCptg4OL/DD625g7y23AzCFT2jXyYbWNEdGWBia1dqrV3Ht6lx1fJYX\n+ijHTIz22CiHDxntb7LRxtGCwDoZu1LSD4xJPL+8hO/WcNMInHvkON9dBqrjOCi7T5TGFZKK5Smt\nV6tZYpcjJOMTM+y70ySBbR8fw7XXMHdgDkLJjtP2AHDbwiLOSEqcvP7oxUxqhNoQpzs+bEqACCFG\nMV3pHoo5098GbgbeB+wB7gB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aSMayIhxi6/TSWuALgZ9mnyoILbnQ1BmnwWjNNPsAEuEj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\nPcYaEIvF0mOsAbFYLD3m/wHBfBGmqHpM4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e79167b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgplot = plt.imshow(img2)\n",
    "imgplot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AzjvxtUQp0qLmm5T74vJDdoshahTeRFkQkawp1WzPL9RbJUKrcG31HaRD54tR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\nUgwb6QKSIkWKYeP/A3eRD3rXXvB4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e79167b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lum_img1 = img2[:, :,1]\n",
    "plt.imshow(lum_img1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6cw/WM716d0IK9rrNDU8ra2oE77M/lrnqu9lXhskt+G33de61TWgudF9je59V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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e79177ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg2 = plt.imshow(lum_img1, cmap=\"Reds\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tq5FjTwVgWeMqltWVJzzYUhj++cptearDPWk+VZowJUqUmDf2bQ0kVvUcv6R2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\npEhRN/4/5JLjRa73n7IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7917d42e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg2=plt.imshow(lum_img1, cmap=\"Oranges\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fg = plt.imshow(lum_img1, cmap=\"Blues\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.image.AxesImage"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(fg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fg1=np.array(fg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(fg1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(<matplotlib.image.AxesImage object at 0x000001E7917B20B8>,\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#####################implementation #############################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import sklearn.datasets\n",
    "imug1 = mpimg.imread('100.png')\n",
    "imug2 = mpimg.imread('20.png')\n",
    "imug3 = mpimg.imread('100.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l1=list(imug1)\n",
    "type(l1)\n",
    "l2=list(imug2)\n",
    "l3=list(imug3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe from zipped list\n",
    "type(l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn.datasets\n",
    "\n",
    "examples = []\n",
    "examples.append('some text')\n",
    "examples.append('another example text')\n",
    "examples.append('example 3')\n",
    "\n",
    "target = np.zeros((3,), dtype=np.int64)\n",
    "target[0] = 0\n",
    "target[1] = 1\n",
    "target[2] = 0\n",
    "dataset = sklearn.datasets.base.Bunch(data=examples, target=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': ['some text', 'another example text', 'example 3'],\n",
       " 'target': array([0, 1, 0], dtype=int64)}"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(examples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn.datasets\n",
    "\n",
    "#examples = []\n",
    "#examples.append('some text')\n",
    "#examples.append('another example text')\n",
    "#examples.append('example 3')\n",
    "\n",
    "#target = np.zeros((3,), dtype=np.int64)\n",
    "#target[0] = 0\n",
    "#target[1] = 1\n",
    "#target[2] = 0\n",
    "dl1 = sklearn.datasets.base.Bunch(data=l1)\n",
    "dl2 = sklearn.datasets.base.Bunch(data=l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn.datasets\n",
    "dl2 = sklearn.datasets.base.Bunch(data=l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn.datasets\n",
    "dl3 = sklearn.datasets.base.Bunch(data=l3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.6       , 0.6039216 , 0.5568628 ],\n",
       "        [0.654902  , 0.5882353 , 0.49019608],\n",
       "        [0.8156863 , 0.75686276, 0.654902  ],\n",
       "        ...,\n",
       "        [0.5058824 , 0.47058824, 0.43529412],\n",
       "        [0.47843137, 0.4509804 , 0.41568628],\n",
       "        [0.43137255, 0.40392157, 0.37254903]],\n",
       "\n",
       "       [[0.6509804 , 0.654902  , 0.6       ],\n",
       "        [0.6392157 , 0.6117647 , 0.53333336],\n",
       "        [0.9137255 , 0.8156863 , 0.6745098 ],\n",
       "        ...,\n",
       "        [0.42745098, 0.4       , 0.36862746],\n",
       "        [0.43529412, 0.4117647 , 0.38039216],\n",
       "        [0.41568628, 0.39215687, 0.36862746]],\n",
       "\n",
       "       [[0.64705884, 0.6509804 , 0.6       ],\n",
       "        [0.627451  , 0.62352943, 0.5686275 ],\n",
       "        [0.85490197, 0.7647059 , 0.6313726 ],\n",
       "        ...,\n",
       "        [0.40784314, 0.38431373, 0.35686275],\n",
       "        [0.4392157 , 0.41568628, 0.38431373],\n",
       "        [0.4392157 , 0.4117647 , 0.38431373]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.34509805, 0.32941177, 0.3019608 ],\n",
       "        [0.32156864, 0.30588236, 0.27450982],\n",
       "        [0.32941177, 0.31764707, 0.28235295],\n",
       "        ...,\n",
       "        [0.35686275, 0.32941177, 0.3019608 ],\n",
       "        [0.39607844, 0.36862746, 0.3372549 ],\n",
       "        [0.39215687, 0.36078432, 0.3254902 ]],\n",
       "\n",
       "       [[0.13725491, 0.13725491, 0.1254902 ],\n",
       "        [0.0627451 , 0.06666667, 0.05882353],\n",
       "        [0.19607843, 0.20784314, 0.20784314],\n",
       "        ...,\n",
       "        [0.3764706 , 0.30588236, 0.22745098],\n",
       "        [0.43529412, 0.34117648, 0.23921569],\n",
       "        [0.5058824 , 0.3882353 , 0.2627451 ]],\n",
       "\n",
       "       [[0.1254902 , 0.12941177, 0.11764706],\n",
       "        [0.07058824, 0.07450981, 0.06666667],\n",
       "        [0.16862746, 0.16862746, 0.16470589],\n",
       "        ...,\n",
       "        [0.7764706 , 0.56078434, 0.32156864],\n",
       "        [0.78431374, 0.5647059 , 0.31764707],\n",
       "        [0.827451  , 0.6039216 , 0.34901962]]], dtype=float32)"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_regressor = LinearRegression()\n",
    "imug1.shape\n",
    "imug2.shape\n",
    "imug1\n",
    "\n",
    "#imug1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "####most important thing image numpy array reshaping########"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6       , 0.654902  , 0.8156863 , ..., 0.7764706 , 0.78431374,\n",
       "        0.827451  ],\n",
       "       [0.6039216 , 0.5882353 , 0.75686276, ..., 0.56078434, 0.5647059 ,\n",
       "        0.6039216 ],\n",
       "       [0.5568628 , 0.49019608, 0.654902  , ..., 0.32156864, 0.31764707,\n",
       "        0.34901962]], dtype=float32)"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imug1_new=imug1.transpose(2,0,1).reshape(3,-1)\n",
    "imug2_new=imug2.transpose(2,0,1).reshape(3,-1)\n",
    "imug1_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 1024)\n",
      "(3, 1024)\n"
     ]
    }
   ],
   "source": [
    "print(imug1_new.shape)\n",
    "print(imug2_new.shape)\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train=pd.DataFrame(imug1_new)\n",
    "test=pd.DataFrame(imug2_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          0         1         2\n",
       "0  0.600000  0.603922  0.556863\n",
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       "4  0.768627  0.741176  0.666667"
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     "execution_count": 514,
     "metadata": {},
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   ],
   "source": [
    "tf = train.transpose()\n",
    "tf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
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       "          0         1         2\n",
       "0  0.486275  0.556863  0.462745\n",
       "1  0.490196  0.556863  0.462745\n",
       "2  0.482353  0.552941  0.466667\n",
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       "4  0.490196  0.552941  0.466667"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt = test.transpose()\n",
    "tt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_numpy = tf.values\n",
    "Y_numpy = tt.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = linear_regressor.fit(X_numpy, Y_numpy)\n",
    "Y_pred = linear_regressor.predict(X_numpy) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.55792516, 0.6028515 , 0.5774427 ],\n",
       "       [0.47173387, 0.49684757, 0.45394322],\n",
       "       [0.5307581 , 0.5827961 , 0.5514275 ],\n",
       "       ...,\n",
       "       [0.21138863, 0.23643576, 0.14348565],\n",
       "       [0.19660902, 0.22709784, 0.1315363 ],\n",
       "       [0.1971724 , 0.23583454, 0.14050972]], dtype=float32)"
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_pred = pd.DataFrame(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "          0         1         2\n",
       "0  0.557925  0.602852  0.577443\n",
       "1  0.471734  0.496848  0.453943\n",
       "2  0.530758  0.582796  0.551427\n",
       "3  0.556069  0.601957  0.575854\n",
       "4  0.570765  0.623952  0.600469"
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     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.41120577, 0.3469096 , 0.29203552], dtype=float32)"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.6159063 , -2.5909011 ,  2.409708  ],\n",
       "       [-0.5370674 , -0.12892216,  1.1781021 ],\n",
       "       [-0.47492653, -0.5147497 ,  1.5824932 ]], dtype=float32)"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn import metrics\n",
    "test_rmse= np.sqrt(metrics.mean_squared_error(Y_pred,tt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12534226"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test_rmse)\n",
    "test_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.55792516, 0.6028515 , 0.5774427 ],\n",
       "       [0.47173387, 0.49684757, 0.45394322],\n",
       "       [0.5307581 , 0.5827961 , 0.5514275 ],\n",
       "       ...,\n",
       "       [0.21138863, 0.23643576, 0.14348565],\n",
       "       [0.19660902, 0.22709784, 0.1315363 ],\n",
       "       [0.1971724 , 0.23583454, 0.14050972]], dtype=float32)"
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.misc\n",
    "Y_pred.shape\n",
    "import imageio\n",
    "import skimage\n",
    "from skimage import img_as_ubyte\n",
    "Y_pred2 = Y_pred.values\n",
    "#imageio.imwrite('picture_out3.jpg', img_as_ubyte(Y_pred2/255.0),cmap='gray')\n",
    "Y_pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12534226"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_rmse\n",
    "\n",
    "##############first finding###############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.55792516, 0.6028515 , 0.5774427 ],\n",
       "       [0.47173387, 0.49684757, 0.45394322],\n",
       "       [0.5307581 , 0.5827961 , 0.5514275 ],\n",
       "       ...,\n",
       "       [0.21138863, 0.23643576, 0.14348565],\n",
       "       [0.19660902, 0.22709784, 0.1315363 ],\n",
       "       [0.1971724 , 0.23583454, 0.14050972]], dtype=float32)"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.4862745 , 0.5568628 , 0.4627451 ],\n",
       "       [0.49019608, 0.5568628 , 0.4627451 ],\n",
       "       [0.48235294, 0.5529412 , 0.46666667],\n",
       "       ...,\n",
       "       [0.39215687, 0.45490196, 0.4509804 ],\n",
       "       [0.39607844, 0.45882353, 0.45882353],\n",
       "       [0.40392157, 0.46666667, 0.4627451 ]], dtype=float32)"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024, 3)"
      ]
     },
     "execution_count": 552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 1., 1.],\n",
       "       ...,\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 557,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoff = 0.55\n",
    "y_pred_classes = np.zeros_like(Y_pred2)\n",
    "y_pred_classes[Y_pred2 > cutoff] = 1\n",
    "\n",
    "y_pred_classes\n",
    "#y_pred_classes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    140\n",
      "1    140\n",
      "2    140\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_classes = np.zeros_like(Y_pred2)\n",
    "y_test_classes[Y_numpy > cutoff] = 1\n",
    "\n",
    "#y_test_classes[:34]\n",
    "y_test_classes[:16]\n",
    "\n",
    "y_test_classes_df=pd.DataFrame(y_test_classes)\n",
    "\n",
    "#y_test_classes_df.head(45)\n",
    "\n",
    "pattern_count_test = y_test_classes_df[(y_test_classes_df[0]== 0) & (y_test_classes_df[1] == 0) & (y_test_classes_df[2] == 0)].count()\n",
    "\n",
    "y_test_classes_df.rename(columns = {0:'R', 1:'G',2:'B'}, inplace = True)\n",
    "\n",
    "\n",
    "print(pattern_count_test)\n",
    "\n",
    "\n",
    "######count for train dataset######\n",
    "\n",
    "y_pred_classes_df = pd.DataFrame(y_pred_classes)\n",
    "pattern_count_train = y_pred_classes_df[(y_pred_classes_df[0]== 0) & (y_pred_classes_df[1] == 0) & (y_pred_classes_df[2] == 0)].count()\n",
    "\n",
    "#y_test_classes_df.rename(columns = {0:'R', 1:'G',2:'B'}, inplace = True)\n",
    "\n",
    "len(y_pred_classes_df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#############Accuracy calculation############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.12370819371591663"
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "model.score(y_test_classes, y_pred_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 1., 1.],\n",
       "       ...,\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "#plot_confusion_matrix(y_test, y_pred, classes=class_names,title='Confusion matrix, without normalization')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test_classes.argmax(axis=1), y_pred_classes.argmax(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[991,   7],\n",
       "       [ 24,   2]], dtype=int64)"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kcxjmI0XErI6UyKyfRI/VMCiaIDUbAW8F7u9Mccz6UC8FjIiYW78v6ULgmo6VyKzPdMst\n0xyjWSd1R+Bl7S6ImXW/nD6Mx/h7pWk94FHg5E4Wyqyv9EqTJM3lOY1iHk+AwYgqLexm1uUq1unZ\nsEmSgsPlETGQtgp9NLOK6LFnSf4gaXrHS2LWr3ohYEiqNVf+kSJo3CVpoaSbJS1cN8Uz620iPYCW\nsWVfUxqTfqdXpv0dJc2XtFTSXEkbpOMbpv1l6fyUZtdu1IfxB2A6cGR+Uc2sJZ1ZW/WjwGJgs7T/\ndeCMiLhE0tnATOC76d/HImIXSUendO9odOFGTRLB86udvWhbyw9kZjVtbJJImgS8Cfh+2hfweuCy\nlOR8/l4JOCLtk84fpCGPpQ/VqIaxtaSTRjoZEac3Lb2ZNZff3Jgg6aa6/dkRMXtImm8CnwY2Tfvj\ngccjYk3aXw5MTK8nkkZtR8QaSatS+pUjFaBRwBhDseJZdR7WN6ugFvonVkbEjBGvIx0OPBQRCyQd\nWDs8TNLIODesRgFjRUR8sdGbzawN2ncH5NXAWyS9keK5r80oahxbSBqbahmTgAdS+uXAZGB5usmx\nOcXAzBE17cMwsw7K7b/ICCoR8dmImBQRUyimpPhlRBwL/Ap4e0p2HPDT9PqKtE86/8tmY60aBYyD\nmhfRzNbWOpjT8zPASZKWUfRRzEnH5wDj0/GTyHjko9HKZw2rJmbWHp0YGh4RvwZ+nV7fA+w7TJpn\ngKNaua4XMjIrW5eM4szhgGFWpi4a9p3DAcOsRKJadxccMMzK5hqGmeWq0nwYDhhmZavQnJ4OGGZl\nqtiMWw4YZmVzwDCzXK5hmFk+Bwwzy+Uahpnl8UhPM8slqrVUogOGWdkqVMMYzdqqWST9QNJDku7o\nVB5mvUARWVs36FjAAM4DDu3g9c2qr40zbq0LHWuSRMRvcxZGMet3vkvSAkmzgFkAGzGu5NKYlcAB\nI19aV2E2wGbrja/QV2fWHq5hmFmeziyV2DEOGGZlq1ANo5O3VS8Grgd2k7Rc0sxO5WVWVZ1Yvb2T\nOnmX5JhOXdusp3TJGIscbpKYlaxbag85HDDMytRFg7JyOGCYlcx3ScwsmwOGmeUJ3OlpZvnc6Wlm\n+RwwzCxHbeBWVThgmJUpwn0YZpbPd0nMLJubJGaWJ4DB6kQMBwyzslUnXnR0EmAzy9Cux9slTZb0\nK0mLJS2S9NF0fCtJ10hamv7dMh2XpLMkLZN0m6TpzfJwwDArW+1OSbOtuTXAJyLiFcB+wAcl7Q6c\nDFwbEVOBa9M+wGHA1LTNAr7bLAMHDLOStauGERErImJhev0ksBiYCBwBnJ+SnQ8cmV4fAVwQhRuA\nLSRt1ygP92GYlUgB6kCnZ1riYy9gPrBtRKyAIqhI2iYlmwjcX/e25enYipGu64BhVrb8cRgTJN1U\ntz87zbr/ApI2AX4MfCwinpA00vWGO9EwejlgmJWshWUQV0bEjIbXktanCBYXRcRP0uEHJW2Xahfb\nAQ+l48uByXVvnwQ80Oj67sMwK1Mbl0pUUZWYAyyOiNPrTl0BHJdeHwf8tO74e9Ldkv2AVbWmy0hc\nwzArVVufJXk18G7gdkm3pGOnAF8DLk0z998HHJXOXQ28EVgGPA0c3ywDBwyzkrVraHhEXMfw/RIA\nBw2TPoAPtpKHA4ZZ2fy0qpllCdCAA4aZ5apOvHDAMCtbC7dVS+eAYVY2BwwzyxK0MtKzdA4YZiUS\n4SaJmbXAAcPMsgTg26pmlstNEjPL54BhZnm8kJGZ5fLq7WbWEo/DMLNc7vQ0szwBDFSniuGAYVYq\nd3qO2pPx6Mprnrno3rLL0YIJwMqyC9Hjqvgdv6yl1A4YoxMRW5ddhlZIuqnZLM62dvriO3bAMLMs\nXr3dzPIFhDs9+8WLVp2ytuvt77hid0m8kNFaGG6ZunqSBiTdIukOST+SNG60eUk6UNKV6fVbJJ3c\nIO0Wkj4wijxOk/TJ3OND0pwn6e0t5DVF0h3N0jX7jntC+1Zv7zgHjM76W0TsGRF7AM8BJ9SfTCtO\ntfz/QURcERFfa5BkC6DlgGElccCwYfwO2CX9ZV0s6b+AhcBkSYdIul7SwlQT2QRA0qGS7pR0HfC2\n2oUkvVfSt9PrbSVdLunWtO1PsdLVzql2842U7lOSbpR0m6Qv1F3rc5LukvQLYLdmH0LS+9N1bpX0\n4yG1poMl/U7SEkmHp/RjJH2jLu9/XdsvsrdkBgsHjP4haSxwGHB7OrQbcEFE7AU8BZwKHBwR04Gb\ngJMkbQScA7wZeA3w0hEufxbwm4iYBkwHFgEnA3en2s2nJB0CTAX2BfYE9pZ0gKS9gaOBvSgC0j4Z\nH+cnEbFPym8xMLPu3BTgtcCbgLPTZ5hJsWbnPun675e0Y0Y+/SGAwcG8rQu407OzNq5b4/J3FAvl\nbg/cGxE3pOP7AbsDvy/W0mUD4Hrg5cCfImIpgKQfArOGyeP1wHsAImIAWCVpyyFpDknbzWl/E4oA\nsilweUQ8nfK4IuMz7SHpSxTNnk2AeXXnLo2IQWCppHvSZzgEeGVd/8bmKe8lGXn1hy6pPeRwwOis\nv0XEnvUHUlB4qv4QcE1EHDMk3Z60b4kbAV+NiO8NyeNjo8jjPODIiLhV0nuBA+vODb1WpLw/HBH1\ngQVJU1rMt3dVKGC4SVK+G4BXS9oFQNI4SbsCdwI7Sto5pTtmhPdfC5yY3jtG0mbAkxS1h5p5wPvq\n+kYmStoG+C3wVkkbS9qUovnTzKbACknrA8cOOXeUpPVSmXcC7kp5n5jSI2lXSS/JyKc/RBADA1lb\nN3ANo2QR8XD6S32xpA3T4VMjYomkWcBVklYC1wF7DHOJjwKzJc0EBoATI+J6Sb9Pty3/N/VjvAK4\nPtVw/gq8KyIWSpoL3ALcS9FsaubfgPkp/e28MDDdBfwG2BY4ISKekfR9ir6NhSoyfxg4Mu/b6RMV\nGumpqFB1yKzXbD5263jVpkdkpZ33+JwFZT9X4xqGWZkiuuYOSA4HDLOyVaiW74BhVrJwDcPM8nTP\nKM4cDhhmZQqgS26Z5nDAMCtRAFGh26oOGGZlCk+gY2YtqFINwwO3zEok6WcUM6PnWBkRh3ayPM04\nYJhZNj98ZmbZHDDMLJsDhpllc8Aws2wOGGaWzQHDzLI5YJhZNgcMM8vmgGFm2f4f2t4+o84s6kAA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e79375d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(cm)\n",
    "plt.title('Confusion matrix')\n",
    "plt.colorbar()\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os.path\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "len(y_pred_classes.shape)\n",
    "\n",
    "#img = Image.fromarray(Y_pred2, 'RGB')\n",
    "final_image_array = np.resize(Y_pred2,(32,32,3))\n",
    "\n",
    "img = Image.fromarray(final_image_array, 'RGB')\n",
    "img.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##########Validation Curve for learning ############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_scores, valid_scores = validation_curve(Ridge(), y_test_classes, y_pred_classes, \"alpha\",np.logspace(-7, 3, 3),cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.49052099, 0.41082007, 0.39937574, 0.44118287, 0.46384058],\n",
       "       [0.49052098, 0.41082006, 0.39937574, 0.44118285, 0.46384057],\n",
       "       [0.16588595, 0.12575613, 0.11351225, 0.13650688, 0.15212106]])"
      ]
     },
     "execution_count": 582,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.16214095, 0.55723914, 0.54968261, 0.39319307, 0.25819758],\n",
       "       [0.16232749, 0.55724817, 0.54967193, 0.3932205 , 0.2582818 ],\n",
       "       [0.00557743, 0.13662625, 0.10211086, 0.12286245, 0.04319383]])"
      ]
     },
     "execution_count": 583,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "valid_scores_mean = np.mean(valid_scores, axis=1)\n",
    "valid_scores_std = np.std(valid_scores, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7937e9ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7939ff7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_range = np.arange(1,3,5)\n",
    "plt.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, color=\"gray\")\n",
    "plt.show()\n",
    "plt.fill_between(param_range, valid_scores_mean - valid_scores_std, valid_scores_mean + valid_scores_std, color=\"gainsboro\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#########R square ##############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1771616477510027"
      ]
     },
     "execution_count": 576,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_pred_classes,y_test_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}