75-persian-digit-classification-with-knn.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><div style=\"direction:rtl;font-family:B Lotus, B Nazanin, Tahoma\">به نام خدا</div></center>\n",
    "<h1><center><div style=\"direction:rtl;font-family:B Lotus, B Nazanin, Tahoma\">طبقه بندی نزدیکترین همسایه و K نزدیکترین همسایه</div></center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">لود کتابخانه‌های مورد استفاده</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from dataset import load_hoda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">لود مجموعه داده (dataset)</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = load_hoda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 25)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">ایجاد طبقه بند k-nn</div>\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "neigh.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">پیش‌بینی داده‌های آموزشی</div>\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 50 is a 7, and you prediction is: 7.\n"
     ]
    }
   ],
   "source": [
    "i = 50\n",
    "\n",
    "X = np.expand_dims(X_test[i], axis=0)\n",
    "\n",
    "predicted_class = neigh.predict(X)\n",
    "\n",
    "print (\"Sample {} is a {}, and you prediction is: {}.\".format(i, y_test[i], predicted_class[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(neigh.predict_proba(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "مثال بالا را با sample سوم تست کنید. و خروجی را تشریح کنید.\n",
    "<br>\n",
    "predict_proba چه احتمالاتی را برگردانده؟ کدام کلاس به عنوان پیش‌بینی مدل انتخاب شده؟\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">پیش‌بینی چندین داده آزمون</div>\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 3, 1, 5, 5, 4, 7, 3, 2, 0, 8, 8, 0, 3, 9, 3, 6, 7, 4, 0, 3,\n",
       "       6, 3, 9, 2, 7, 5, 2, 9, 7, 5, 5, 8, 9, 6, 5, 1, 4, 8, 8, 4, 2, 7,\n",
       "       1, 2, 7, 9, 0, 3, 7, 4, 7, 5, 2, 9, 8, 2, 9, 8, 8, 6, 6, 6, 7, 6,\n",
       "       2, 4, 1, 4, 4, 5, 9, 1, 8, 2, 0, 5, 6, 2, 4, 3, 2, 7, 7, 7, 7, 1,\n",
       "       8, 1, 7, 8, 7, 7, 8, 9, 3, 2, 3, 1, 0, 2, 9, 7, 3, 5, 5, 0, 0, 2,\n",
       "       6, 7, 9, 3, 9, 9, 8, 7, 9, 2, 5, 2, 5, 5, 9, 6, 9, 2, 0, 3, 7, 6,\n",
       "       5, 2, 9, 0, 4, 1, 8, 2, 2, 3, 0, 2, 9, 3, 8, 6, 7, 0, 9, 9, 0, 7,\n",
       "       6, 1, 4, 7, 9, 3, 7, 0, 7, 1, 9, 4, 7, 3, 4, 1, 5, 6, 7, 9, 1, 3,\n",
       "       5, 4, 5, 7, 4, 1, 3, 3, 1, 2, 5, 3, 8, 9, 6, 7, 7, 2, 3, 0, 1, 4,\n",
       "       9, 5], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_classes = neigh.predict(X_test)\n",
    "pred_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">ارزیابی</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 96.00 %\n"
     ]
    }
   ],
   "source": [
    "acc = neigh.score(X_test, y_test)\n",
    "print (\"Accuracy is %.2f %%\" %(acc*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://class.vision/product/intro-to-ai-ml-deep/"
   ]
  }
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