{"id":31351,"date":"2024-10-28T12:01:24","date_gmt":"2024-10-28T12:01:24","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=31351"},"modified":"2024-11-26T06:45:25","modified_gmt":"2024-11-26T06:45:25","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d%ea%b3%bc-%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d%ec%9d%84-%ed%99%9c%ec%9a%a9%ed%95%9c-%ec%9e%90%eb%8f%99%eb%a7%a4%eb%a7%a4-ensemble-learning-%ea%b8%b0%eb%b0%98-%eb%a7%a4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/31351\/","title":{"rendered":"\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, Ensemble Learning \uae30\ubc18 \ub9e4\ub9e4 \uc804\ub7b5 \uc5ec\ub7ec \ubaa8\ub378\uc744 \uacb0\ud569\ud55c \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \ud1b5\ud574 \ub354 \uc815\ud655\ud55c \ub9e4\ub9e4 \uc2e0\ud638 \uc0dd\uc131."},"content":{"rendered":"

<\/p>\n

\ube44\ud2b8\ucf54\uc778\uacfc \uac19\uc740 \uc554\ud638\ud654\ud3d0\uc758 \uc2dc\uc7a5\uc740 \ub9e4\uc6b0 \ubcc0\ub3d9\uc131\uc774 \ud06c\uba70, \ub9e4\uc77c \uc218\ub9ce\uc740 \uac70\ub798\uac00 \uc774\ub8e8\uc5b4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc2dc\uc7a5 \ud658\uacbd\uc5d0\uc11c \uc218\uc775\uc744 \ucc3d\ucd9c\ud558\uae30 \uc704\ud574\uc11c\ub294 \uace0\ub3c4\ud654\ub41c \ub9e4\ub9e4 \uc804\ub7b5\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5(AI) \uae30\uc220\uc758 \ubc1c\uc804\uc73c\ub85c, \ud2b9\ud788 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \ub354\uc6b1 \uac01\uad11\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uc559\uc0c1\ube14 \ud559\uc2b5\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uc744 \uc2ec\ub3c4 \uc788\uac8c \uc124\uba85\ud558\uace0, \uc774\ub97c \uc704\ud55c \uc608\uc81c \ucf54\ub4dc\ub97c \uc81c\uacf5\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uc559\uc0c1\ube14 \ud559\uc2b5\uc774\ub780?<\/h2>\n

\uc559\uc0c1\ube14 \ud559\uc2b5(Ensemble Learning)\uc740 \uc5ec\ub7ec \uac1c\uc758 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uacb0\ud569\ud558\uc5ec \ub354 \ub098\uc740 \uc608\uce21 \uc131\ub2a5\uc744 \ub3c4\ubaa8\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uac01\uac01\uc758 \ubaa8\ub378\uc774 \uac1c\ubcc4\uc801\uc73c\ub85c \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud55c \uacb0\uacfc\ub97c \uc870\ud569\ud568\uc73c\ub85c\uc368, \ub2e8\uc77c \ubaa8\ub378\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uc624\ucc28\ub97c \uc904\uc774\uace0, \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

\uc559\uc0c1\ube14 \ud559\uc2b5\uc758 \uc8fc\uc694 \ubc29\ubc95\uc5d0\ub294 \ubc30\uae45(Bagging)<\/strong>, \ubd80\uc2a4\ud305(Boosting)<\/strong>, \uc2a4\ud0dc\ud0b9(Stacking)<\/strong> \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

1.1 \ubc30\uae45<\/h3>\n

\ubc30\uae45\uc740 \ub370\uc774\ud130\ub97c \uc5ec\ub7ec \uac1c\uc758 \ubd80\ubd84 \uc9d1\ud569\uc73c\ub85c \ub098\ub208 \ud6c4 \uac01 \ubd80\ubd84 \uc9d1\ud569\uc5d0 \ub300\ud574 \ub3c5\ub9bd\uc801\uc73c\ub85c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4. \ucd5c\uc885 \uc608\uce21\uc740 \uac01 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \ud3c9\uade0\ub0b4\uac70\ub098 \ub2e4\uc218\uacb0\ub85c \uacb0\uc815\ud569\ub2c8\ub2e4. \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8(Random Forest)\uac00 \ub300\ud45c\uc801\uc778 \ubc30\uae45 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4.<\/p>\n

1.2 \ubd80\uc2a4\ud305<\/h3>\n

\ubd80\uc2a4\ud305\uc740 \uc774\uc804 \ubaa8\ub378\uc758 \uc624\ub958\ub97c \uc218\uc815\ud558\ub294 \ud615\ud0dc\ub85c \ub2e4\uc74c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uac01 \ubaa8\ub378\uc774 \uc21c\ucc28\uc801\uc73c\ub85c \ud559\uc2b5\ud558\uba70, \uc57d\ud55c \ud559\uc2b5\uae30(weak learner)\ub97c \uc5ec\ub7ec \uac1c \uacb0\ud569\ud558\uc5ec \uac15\ud55c \ud559\uc2b5\uae30(strong learner)\ub97c \ub9cc\ub4dc\ub294 \uacfc\uc815\uc744 \uac70\uce69\ub2c8\ub2e4. AdaBoost\uc640 XGBoost\uac00 \uc720\uba85\ud55c \ubd80\uc2a4\ud305 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4.<\/p>\n

1.3 \uc2a4\ud0dc\ud0b9<\/h3>\n

\uc2a4\ud0dc\ud0b9\uc740 \uc5ec\ub7ec \ubaa8\ub378\uc744 \ud559\uc2b5 \ud6c4, \uc0c8\ub85c\uc6b4 \ubaa8\ub378(\uba54\ud0c0\ubaa8\ub378)\uc744 \uc0ac\uc6a9\ud574 \uac01 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \ud559\uc2b5\ud558\uc5ec \ucd5c\uc885 \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \ub2e4\uc591\ud55c \ubaa8\ub378\uc758 \uc7a5\uc810\uc744 \ucde8\ud569\ud558\uc5ec \ub354\uc6b1 \uac15\ub825\ud55c \uc608\uce21\ub825\uc744 \uac16\ucd98 \ubaa8\ub378\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2. \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uc124\uacc4<\/h2>\n

\uc774\ubc88 \uc139\uc158\uc5d0\uc11c\ub294 CNN(\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd)\uacfc LSTM(\uc7a5\ub2e8\uae30 \uae30\uc5b5 \ub124\ud2b8\uc6cc\ud06c) \ubaa8\ub378\uc744 \uacb0\ud569\ud55c \uc608\uc81c \uc2dc\uc2a4\ud15c\uc744 \uc124\uacc4\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \uc5ed\uc0ac\uc801\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc744 \uc608\uce21\ud558\uace0, \uadf8 \uacb0\uacfc\ub97c \ubc14\ud0d5\uc73c\ub85c \ub9e4\ub9e4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n

2.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n

\ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub294 \uc5ec\ub7ec API\ub97c \ud1b5\ud574 \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 yfinance<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud574 \uacfc\uac70 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uac00\uc838\uc624\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

2.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n

\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub97c \ubaa8\ub378\uc5d0 \ub9de\uac8c \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uacb0\uce21\uce58\ub97c \ucc98\ub9ac\ud558\uace0, \uac00\uaca9 \ub370\uc774\ud130\ub97c \uc815\uaddc\ud654(normalization)\ud558\ub294 \uac83\uc774 \uc77c\ubc18\uc801\uc785\ub2c8\ub2e4.<\/p>\n

2.3 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n

\ud6c8\ub828\ud560 \ubaa8\ub378\uc740 CNN\uacfc LSTM\uc744 \uc870\ud569\ud574\uc11c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. CNN\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0\uc11c \uc911\uc694\ud55c \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub418\uba70, LSTM\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc758 \uc21c\uc11c \uc815\ubcf4\uc640 \uc7a5\uae30 \uc758\uc874\uc131\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/p>\n

2.4 \ub9e4\ub9e4 \uc2e0\ud638 \uc0dd\uc131<\/h3>\n

\ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21\uc744 \uc218\ud589\ud558\uace0, \ud2b9\uc815 \uc784\uacc4\uac12(threshold)\uc5d0 \ub530\ub77c \ub9e4\uc218 \ub610\ub294 \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc608\uce21\ub41c \uac00\uaca9\uc774 \ud604\uc7ac \uac00\uaca9\ubcf4\ub2e4 \ub192\uc73c\uba74 \ub9e4\uc218 \uc2e0\ud638, \ub0ae\uc73c\uba74 \ub9e4\ub3c4 \uc2e0\ud638\ub97c \ubcf4\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

3. \uc608\uc81c \ucf54\ub4dc<\/h2>\n

\uc774\uc81c \uc2e4\uc81c \ucf54\ub4dc \uc608\uc81c\ub97c \ud1b5\ud574 \uc704\uc5d0\uc11c \uc124\uba85\ud55c \ub0b4\uc6a9\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

\n    \nimport numpy as np\nimport pandas as pd\nimport yfinance as yf\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Conv1D, Dense, Flatten, Dropout\n\n# \ube44\ud2b8\ucf54\uc778 \ub370\uc774\ud130 \uc218\uc9d1\ndata = yf.download('BTC-USD', start='2020-01-01', end='2023-10-01')\ndata = data['Close'].values\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac\nscaler = MinMaxScaler(feature_range=(0, 1))\nscaled_data = scaler.fit_transform(data.reshape(-1, 1))\n\n# \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \uc0dd\uc131 \ud568\uc218\ndef create_dataset(data, time_step=1):\n    X, y = [], []\n    for i in range(len(data) - time_step - 1):\n        X.append(data[i:(i + time_step), 0])\n        y.append(data[i + time_step, 0])\n    return np.array(X), np.array(y)\n\ntime_step = 60\nX, y = create_dataset(scaled_data, time_step)\nX = X.reshape(X.shape[0], X.shape[1], 1)\n\n# \ub370\uc774\ud130 \ubd84\ub9ac\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# LSTM \ubaa8\ub378 \uc0dd\uc131\nmodel = Sequential()\nmodel.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1)))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(50, return_sequences=True))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(50))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(1))\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n\n# \ubaa8\ub378 \ud6c8\ub828\nmodel.fit(X_train, y_train, epochs=50, batch_size=32)\n\n# \uc608\uce21\npredicted_price = model.predict(X_test)\npredicted_price = scaler.inverse_transform(predicted_price)\n\n# \uacb0\uacfc \uc2dc\uac01\ud654\nplt.figure(figsize=(10,6))\nplt.plot(scaler.inverse_transform(y_test.reshape(-1, 1)), color='blue', label='\uc2e4\uc81c \uac00\uaca9')\nplt.plot(predicted_price, color='red', label='\uc608\uce21 \uac00\uaca9')\nplt.title('\ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21')\nplt.xlabel('\uc2dc\uac04')\nplt.ylabel('\uac00\uaca9')\nplt.legend()\nplt.show()\n    <\/code>\n    <\/pre>\n
\uc704 \ucf54\ub4dc\ub294 \ube44\ud2b8\ucf54\uc778 \uac00\uaca9\uc744 \uc608\uce21\ud558\uae30 \uc704\ud55c \uac04\ub2e8\ud55c LSTM \ubaa8\ub378\uc744 \uad6c\ud604\ud55c \uc608\uc785\ub2c8\ub2e4. \uc2e4\uc81c \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc774 \ub418\uae30 \uc704\ud574\uc11c\ub294 \ucd94\uac00\uc801\uc778 \ub85c\uc9c1\uc744 \ub354\ud574 \ub9e4\ub9e4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\ub294 \ubd80\ubd84\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
4. \ub9e4\ub9e4 \uc804\ub7b5 \uad6c\ud604<\/h2>\n
\ubaa8\ub378\uc774 \ud6c8\ub828\ub41c \ud6c4, \uc608\uce21\ub41c \uac00\uaca9\uc744 \ubc14\ud0d5\uc73c\ub85c \ub9e4\ub9e4 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\ub294 \ub2e8\uacc4\ub85c \ub118\uc5b4\uac11\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \ub2e8\uc21c\ud55c \uc804\ub7b5\uc73c\ub85c, \uc608\uce21\ub41c \uac00\uaca9\uacfc \uc2e4\uc81c \uac00\uaca9\uc758 \ucc28\uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \ub9e4\ub9e4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\n    \n# \ub9e4\ub9e4 \uc2e0\ud638 \uc0dd\uc131\ndef generate_signals(predicted_prices, actual_prices):\n    signals = np.zeros(len(predicted_prices))\n    for i in range(1, len(predicted_prices)):\n        if predicted_prices[i] > actual_prices[i-1]:\n            signals[i] = 1  # \ub9e4\uc218 \uc2e0\ud638\n        elif predicted_prices[i] < actual_prices[i-1]:\n            signals[i] = -1  # \ub9e4\ub3c4 \uc2e0\ud638\n    return signals\n\nsignals = generate_signals(predicted_price.flatten(), scaler.inverse_transform(y_test.reshape(-1, 1)).flatten())\n    <\/code>\n    <\/pre>\n
5. \uc131\ub2a5 \ud3c9\uac00<\/h2>\n
\uc644\uc131\ub41c \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \uac83\uc740 \uc911\uc694\ud569\ub2c8\ub2e4. \uc5ec\ub7ec \uc9c0\ud45c\ub97c \ud1b5\ud574 \uc2dc\uc2a4\ud15c\uc758 \uc131\uacf5 \uc5ec\ubd80\ub97c \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc218\uc775\ub960, \ucd5c\ub300 \uc190\uc2e4, \uc0e4\ud504 \ube44\uc728 \ub4f1\uc758 \uc9c0\ud45c\ub97c \uc774\uc6a9\ud574 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\n    \n# \uc131\ub2a5 \ud3c9\uac00\ndef evaluate_performance(signals, actual_prices):\n    returns = np.zeros(len(signals))\n    for i in range(len(signals)-1):\n        if signals[i] == 1:  # \ub9e4\uc218\n            returns[i+1] = actual_prices[i+1] \/ actual_prices[i] - 1\n        elif signals[i] == -1:  # \ub9e4\ub3c4\n            returns[i+1] = -1 * (actual_prices[i+1] \/ actual_prices[i] - 1)\n    return np.cumprod(1 + returns) - 1\n\ncumulative_returns = evaluate_performance(signals, scaler.inverse_transform(y_test.reshape(-1, 1)).flatten())\n\n# \uacb0\uacfc \uc2dc\uac01\ud654\nplt.figure(figsize=(10,6))\nplt.plot(cumulative_returns, color='green', label='\ub204\uc801 \uc218\uc775\ub960')\nplt.title('\ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uc131\ub2a5')\nplt.xlabel('\uc2dc\uac04')\nplt.ylabel('\uc218\uc775\ub960')\nplt.legend()\nplt.show()\n    <\/code>\n    <\/pre>\n
6. \ub9c8\ubb34\ub9ac<\/h2>\n
\uc774\ubc88 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uc559\uc0c1\ube14 \ud559\uc2b5 \uc6d0\uce59\uc744 \ubc14\ud0d5\uc73c\ub85c \uc124\uacc4\ud558\uace0 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc559\uc0c1\ube14 \ud559\uc2b5\uc740 \ub2e4\uc591\ud55c \ubaa8\ub378\uc758 \uc7a5\uc810\uc744 \uacb0\ud569\ud558\uc5ec \uc608\uce21 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uc720\uc6a9\ud55c \uae30\ubc95\uc785\ub2c8\ub2e4. \uc2e4\uc81c \ub9e4\ub9e4 \ud658\uacbd\uc5d0\uc11c\ub294 \ub354\uc6b1 \uc815\ubc00\ud55c \ub9e4\ub9e4 \uc804\ub7b5\uc774 \ud544\uc694\ud558\uba70, \uc774 \uc678\uc5d0\ub3c4 \ub2e4\uc591\ud55c \uace0\uae09 \uc54c\uace0\ub9ac\uc998\uacfc \uae30\ubc95\uc744 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc55e\uc73c\ub85c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uac1c\uc120\ud558\uace0 \ubc1c\uc804\uc2dc\ud0a4\uae30 \uc704\ud574 \ub354 \ub9ce\uc740 \uc2e4\ud5d8\uacfc \uc5f0\uad6c\ub97c \uc9c4\ud589\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ub3c5\uc790 \uc5ec\ub7ec\ubd84\ub4e4\ub3c4 \uc790\uc2e0\ub9cc\uc758 \ub9e4\ub9e4 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\uace0, \uc774\ub97c \uc2e4\ud5d8\ud574\ubcf4\uae30\ub97c \uad8c\uc7a5\ud569\ub2c8\ub2e4.<\/p>\n
\uc544\uc6b8\ub7ec, \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc740 \ub9e4\uc6b0 \ubcc0\ub3d9\uc131\uc774 \ud06c\uace0 \uc704\ud5d8\ub3c4\uac00 \ub192\uc740 \uc2dc\uc7a5\uc785\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc2e4\uc81c \uac70\ub798\ub97c \ud589\ud558\uae30 \uc804\uc5d0 \ucda9\ubd84\ud55c \uc5f0\uad6c\uc640 \uac80\ud1a0\uac00 \ud544\uc694\ud568\uc744 \uc78a\uc9c0 \ub9c8\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
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