lab-02-1-linear_regression_plot.ipynb

{
    "nbformat_minor": 2, 
    "cells": [
        {
            "source": "---\n* *** \uc6d0\ubcf8 \uc18c\uc2a4\ub294 \ud64d\ucf69\uacfc\uae30\ub300 \uae40\uc131\ud6c8 \uad50\uc218\ub2d8\uc758  [Github link](https://github.com/hunkim/DeepLearningZeroToAll)\ub97c \ucc38\uc870\ud558\uc138\uc694 ***     \n* *** Watson studio notebook \uc5d0\uc11c \uc791\uc5c5 \uac00\ub2a5\ud558\ub3c4\ub85d \uc218\uc815\ud558\uc600\uc73c\uba70***\n* *** \ud55c\uae00\ub85c \uc124\uba85\ub41c \ubd80\ubd84\uc740 \uc81c\uac00 \uc2a4\ud130\ub514 \ud558\uba74\uc11c \uc774\ud574\ud55c \ub0b4\uc6a9\uc744 \ucd94\uac00\ud55c \uac83\uc785\ub2c8\ub2e4. ***\n---", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "## Lab 2-1 Linear Regression with plot\n* ***\uc5ec\uae30\uc11c\ub294 \uadf8\ub798\ud504\ub97c \uadf8\ub824\uac00\uba74\uc11c linear regression \uc774 \uc5b4\ub5bb\uac8c \uc791\ub3d9\ud558\ub294 \uc9c0\ub97c \uc774\ud574\ud569\ub2c8\ub2e4.***\n* ***\uc544\ub798\ucabd\uc5d0 matplotlib \uc608\uc2dc \ucf54\ub4dc\ub97c \ucc38\uc870\ud574\uc11c \uadf8\ub798\ud504 \ubaa8\uc591\uc744 \ubc14\uafb8\uc5b4 \ubd05\ub2c8\ub2e4.***\n* ***\uadf8\ub798\ud504 \ubcc0\uc218\ub4e4\ub3c4 \ub2e4\uc591\ud558\uac8c \ubc14\uafd4 \uac00\uba74\uc11c \ub3cc\ub824 \ubd05\ub2c8\ub2e4.***", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 1, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# Lab 2 Linear Regression\nimport tensorflow as tf\ntf.set_random_seed(777)  # for reproducibility\nimport matplotlib.pyplot as plt\nimport numpy as np\n%matplotlib inline"
        }, 
        {
            "execution_count": 2, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# X and Y data\nx_train = [1, 2, 3]\ny_train = [0.5, 1, 1.5]\n\n# Try to find values for W and b to compute y_data = x_data * W + b\n# We know that W should be 1 and b should be 0\n# But let TensorFlow figure it out\nW = tf.Variable(tf.random_normal([1]), name='weight')\nb = tf.Variable(tf.random_normal([1]), name='bias')\n\n\n# Our hypothesis XW+b\nhypothesis = x_train * W + b\n\n# cost/loss function\ncost = tf.reduce_mean(tf.square(hypothesis - y_train))\n\n# Minimize\noptimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)\ntrain = optimizer.minimize(cost)\n\n# Launch the graph in a session.\nsess = tf.Session()\n\n# \uadf8\ub798\ud504\ub97c \uadf8\ub9ac\uae30 \uc704\ud55c \ubcc0\uc218\ub4e4\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.\nx = [] \ny1 = []\ny2 = []\ny3 = []\ny4 = []\n\n# Initializes global variables in the graph.\nsess.run(tf.global_variables_initializer())"
        }, 
        {
            "source": "", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 3, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "0 6.92023 [ 2.08201027] [-0.87235665]\n50 0.194762 [ 1.01530433] [-1.16378677]\n100 0.15306 [ 0.95439523] [-1.03292608]\n150 0.120319 [ 0.90286809] [-0.91581362]\n200 0.0945816 [ 0.85718995] [-0.81197673]\n250 0.0743498 [ 0.81669104] [-0.71991307]\n300 0.0584457 [ 0.78078389] [-0.63828778]\n350 0.0459436 [ 0.74894804] [-0.56591743]\n400 0.0361159 [ 0.72072184] [-0.50175261]\n450 0.0283904 [ 0.69569588] [-0.44486281]\n500 0.0223174 [ 0.67350745] [-0.39442325]\n550 0.0175436 [ 0.65383488] [-0.34970275]\n600 0.0137908 [ 0.63639271] [-0.31005266]\n650 0.0108408 [ 0.62092823] [-0.27489823]\n700 0.00852188 [ 0.60721701] [-0.24372956]\n750 0.00669898 [ 0.59506059] [-0.21609499]\n800 0.005266 [ 0.58428234] [-0.19159359]\n850 0.00413956 [ 0.57472622] [-0.16987029]\n900 0.00325407 [ 0.5662536] [-0.15061004]\n950 0.002558 [ 0.55874163] [-0.13353363]\n1000 0.00201082 [ 0.55208135] [-0.11839324]\n1050 0.00158069 [ 0.54617631] [-0.10496957]\n1100 0.00124257 [ 0.54094088] [-0.09306797]\n1150 0.000976772 [ 0.53629881] [-0.08251572]\n1200 0.000767832 [ 0.53218323] [-0.0731599]\n1250 0.000603585 [ 0.52853417] [-0.06486495]\n1300 0.000474475 [ 0.52529889] [-0.0575104]\n1350 0.000372981 [ 0.52243054] [-0.05098978]\n1400 0.000293196 [ 0.51988709] [-0.04520839]\n1450 0.000230478 [ 0.51763231] [-0.04008247]\n1500 0.000181176 [ 0.51563311] [-0.03553782]\n1550 0.00014242 [ 0.51386064] [-0.03150843]\n1600 0.000111956 [ 0.51228911] [-0.02793596]\n1650 8.80077e-05 [ 0.51089573] [-0.02476856]\n1700 6.9182e-05 [ 0.5096603] [-0.02196022]\n1750 5.43833e-05 [ 0.50856501] [-0.01947025]\n1800 4.27497e-05 [ 0.50759387] [-0.01726266]\n1850 3.36051e-05 [ 0.50673282] [-0.01530537]\n1900 2.64168e-05 [ 0.50596946] [-0.01357]\n1950 2.07659e-05 [ 0.50529265] [-0.0120314]\n2000 1.63239e-05 [ 0.50469249] [-0.01066726]\n2050 1.28322e-05 [ 0.50416046] [-0.00945774]\n2100 1.00869e-05 [ 0.50368875] [-0.00838539]\n2150 7.92928e-06 [ 0.50327057] [-0.00743467]\n2200 6.2334e-06 [ 0.50289971] [-0.00659171]\n2250 4.89992e-06 [ 0.50257099] [-0.00584433]\n2300 3.85177e-06 [ 0.50227946] [-0.00518169]\n2350 3.02784e-06 [ 0.50202101] [-0.00459422]\n2400 2.38023e-06 [ 0.50179189] [-0.00407332]\n2450 1.87106e-06 [ 0.50158876] [-0.00361152]\n"
                }, 
                {
                    "output_type": "display_data", 
                    "data": {
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wVO4S16qrf01ycjZjxlzgdRSREaVyl7gVCHRRW/s8hYWXkpw8yus4IiNK5S5xq6HhNfz+BoqKrvQ6isiIU7lL3Kqu/jUpKXnk52tJRhKPyl3iUk9PB7W1L1BYeBlJSWlexxEZcSp3iUv19S/T09PM2LGLvY4i4gmVu8Slmppfk5paSF7euV5HEfGEyl3iTk9PG7W1L1JYeLlupycJS+Uucaeu7iUCgVbGjtVZMpK4VO4Sd6qrl5KWdgx5eWd6HUXEMyp3iSt+fwv19cspKvosZslexxHxjMpd4kpNzXMEAh2MHXuV11FEPKVyl7hSVfUEmZmzyMk5zesoIp5SuUvcaG3dRnPz2xxzzA2YmddxRDwVVrmb2UIzKzezCjO78wjjrjAzZ2bzIxdRJDwHDjyJWQrHHPMFr6OIeG7AcrfgUalHgAuBOcBVZjann3HZwK3AnyMdUmQggUA3Bw48TUHBRaSljfM6jojnwpm5nwpUOOc+dM51AUuBS/sZ9wPgHqAjgvlEwlJfv5zu7oMcc8z1XkcRiQrhlPtEYG+vx5Whrx1iZnOBY51zfzjSC5nZTWa2zszW1dTUDDqsyOFUVT1BWtox5Odf6HUUkagQTrn3d2TKHXoyeGPKB4CvD/RCzrklzrn5zrn5RUVF4acUOYLOzirq6l5i3Lhrtd2ASEg45V4JHNvrcTGwv9fjbOAE4E0z2wWcBizTQVUZKQcPPgP0MH68lmREPhJOua8FZpjZFDNLAxYDyz560jnX5JwrdM6VOOdKgNXAJc65dcOSWKQX53rYv/8xcnMXkJk50+s4IlFjwHJ3zvmBrwKvANuA55xzW8zsbjPTLeXFU3V1L9HRsZOJE2/1OopIVAlrgdI5txxY3udrdx1m7NlHH0skPJWVPyE9fRKFhf/L6ygiUUVXqErM8vneo7FxBRMn/oMOpIr0oXKXmLVv30MkJY1i/PgbvY4iEnVU7hKTurpqOXjwl4wb9wVSU/O9jiMSdVTuEpOqqn5OINBBcbEOpIr0R+UuMaenp4N9+x5mzJhPk5V1vNdxRKKSjkJJzDlw4Cm6uvYze/bTXkcRiVqauUtMCQS62bPnR+TknE5e3rlexxGJWpq5S0w5ePAZOjt3M3Pmo7ohh8gRaOYuMSMQ8LN7978wevQ88vMXeh1HJKpp5i4xo7p6KR0dOzj++N9p1i4yAM3cJSYEAl3s3v19srJKKSzUlkYiA9HMXWLC/v3/Tnt7BaWlLxG8hYCIHIneJRL1/P4mdu36Pnl55+pOSyJhUrlL1Nuz58f4/XVMm3aP1tpFwqRyl6jW0bGXysoHGDv282Rnz/M6jkjMULlLVKuo+BpgTJ36/7yOIhJTdEBVolZt7YvU1v6OqVN/REbGZK/jiMQUzdwlKvX0tPLBB7eQmXk8xcV3eB1HJOZo5i5RaefOu+js3M1JJ71FUlKq13FEYo5m7hJ1Ghv/m8rKB5gw4cvk5S3wOo5ITFK5S1Tx+5vZtu1aRo2azrRp93odRyRmaVlGooZzjvff/zKdnXuZO/dPJCdneR1JJGap3CVq7N//KNXVz1JS8gNyc0/zOo5ITNOyjESFpqbVVFTcRn7+Z5g8+dtexxGJeSp38Vx7+042b76E9PRjmT37GW0MJhIBeheJp7q6annvvc/gnJ+ysuWkpo7xOpJIXNCau3imq6uWjRvPo6NjJ2VlL5OZeZzXkUTihmbu4omurho2bjyX9vb3OeGEF8nLO8vrSCJxReUuI66jY0+o2CsoLf0D+fmf9jqSSNzRsoyMqKamP7F58/8mEOigtPQPjBlzrteRROKSZu4yIpwLUFn5UzZsOIeUlBxOPnm1il1kGGnmLsOuo2M327dfT2PjCvLzFzF79i91VozIMFO5y7AJBLrYt+9n7Np1F+CYOfPnjB9/g26VJzICVO4Scc456ur+wI4dX6e9/QPGjLmAmTMfY9SoKV5HE0kYYa25m9lCMys3swozu7Of5+8ws61mtsnMXjcz3TYnQfl8m9i48Xw2b74ESKK09CXKyl5WsYuMsAFn7maWDDwCnA9UAmvNbJlzbmuvYe8C851zbWb2ZeAe4MrhCCzRqbPzALt2/TNVVb8gJWUM06c/xIQJN+tGGyIeCWdZ5lSgwjn3IYCZLQUuBQ6Vu3PujV7jVwNXRzKkRK9AwE9l5f3s3v1DAoFOiotvY/Lkf9YBUxGPhVPuE4G9vR5XAp84wvgbgD/294SZ3QTcBDBp0qQwI0q0am3dxvbt19LSspaCgkuZNu1eMjNneB1LRAiv3Ps7tcH1O9DsamA+0O+15M65JcASgPnz5/f7GhL9nHPs3/8YFRW3k5w8mjlznmPs2M96HUtEegmn3CuBY3s9Lgb29x1kZp8G/gk4yznXGZl4Em16elopL/8/VFf/J/n5FzJr1pOkpY3zOpaI9BFOua8FZpjZFGAfsBj4XO8BZjYX+HdgoXOuOuIpJSq0tm5ny5bLaWvbzpQpP2TSpG9p73WRKDVguTvn/Gb2VeAVIBl4wjm3xczuBtY555YB9wKjgf8KXaCyxzl3yTDmlhFWU/Nbtm+/jqSkUZx44v9nzJjzvI4kIkcQ1kVMzrnlwPI+X7ur1+fa1i9OBQJ+du78Nnv33kt29ic4/vjfkJFR7HUsERmArlCVw+rqqmbr1itpbHyTCRO+wvTp/0ZSUrrXsUQkDCp36VdT02q2bLkCv7+OWbP+g2OOucbrSCIyCCp3+RvB0xwfpaLiNtLTi5k79x2ys0/yOpaIDJLKXQ7p6Wnj/fe/zMGDT2trXpEYp3IXANrbd7B58+W0tm6ipOR7TJ78zzrNUSSGqdyFmprfUl5+I2CUlr5EQcGFXkcSkaOkck9gPT2tVFTcTlXVz8nOPoU5c5YyatRUr2OJSASo3BOUz7eRrVsX09ZWzqRJd1JScre25xWJIyr3BOOcY9++h9ix4/+SmlrAiSe+qqtNReKQyj2BdHbuo7z8RurrX6ag4CKOO+5J0tIKvY4lIsNA5Z4AnHMcPPhLKipuJRDoYsaMh5kw4Su6UbVIHFO5x7nOzgO8//7N1NX9npycTzJr1lNkZk73OpaIDDOVe5xyzlFT8xzvv/8VenpamTbtPoqLbyN4S1wRiXcq9zjU0bGHDz64hbq6ZWRnn8KsWf9BVtZsr2OJyAhSuceRQMDPvn0Ps3PndwDH1Kn3UFx8O0lJ+msWSTR618eJlpb1lJffhM/3F/LzL2TGjJ8xalSJ17FExCMq9xjX3d3Arl3fY9++h0lLG8ucOc9RVHSFzoQRSXAq9xjlXA9VVY+zc+d36O6uY8KEm5ky5V9ITc3zOpqIRAGVewxqaHiTioqv0dq6idzcs5g+/UHtuS4if0PlHkPa2irYufNb1NT8hvT0ycyZ818UFV2uJRgR+RiVewzo7NzP7t0/oKrqcczSKCn5Acce+3WSk0d5HU1EopTKPYp1dzewd+89VFb+BOf8TJhwM5Mnf4e0tHFeRxORKKdyj0J+fxP79j3M3r334fc3MW7c5ykp+b72WheRsKnco0hXVy379v2Eysqf0tPTREHBxUyZ8kNGjy7zOpqIxBiVexTo7DxAZeX97Nv3KIFAK4WFlzN58j+RnT3X62giEqNU7h7y+TZSWfkgBw8+i3N+xo69ismTv0VW1vFeRxORGKdyH2HO9VBX9xKVlQ/S2PgGSUmZjB9/I8XFt2srXhGJGJX7COnsPMDBg0+zf/8SOjp2kJ4+ialT72X8+BtITR3jdTwRiTMq92HkXA/19a9QVfU4dXUv4pyf3NwFTJ36rxQWXqbdGkVk2KhdIsw5R2vrJqqrl3Lw4C/p7KwkNbWI4uLbGT/+BjIzj/M6oogkAJV7hLS1fUB19a+orl5KW9s2IJn8/POZPv1BCgouJikpzeuIIpJAVO5D5FyAlpZ11NW9SG3ti7S2bgQgN/dMZsy4haKiK0hLK/I4pYgkKpX7IHR3N9DY+CZ1dS9RX/8SXV0HgCRyc89g2rT7KCq6koyMYq9jioio3I/E72+hqWklDQ0raGx8A5/vXcCRnJxDfv5CCgoupqDgQlJTC7yOKiLyN1TuIYFAN62tm2lpWUNz8xpaWtbQ2roVCGCWRk7O6ZSUfJe8vHPIyTlNa+giEtXCKnczWwj8BEgGHnfO/ajP8+nA08A8oA640jm3K7JRIyMQ6KSjYxetrVtpa9tKa+uW0Ofbca4TgNTUQrKzT6Ww8HLy8s4kJ+d0ba8rIjFlwHI3s2TgEeB8oBJYa2bLnHNbew27AWhwzk03s8XAj4ErhyNwb845AoGOQx89PT66u+vo7q6lu7sWv7+Orq6DdHTspqNjN52du0Pr5H+Vnj6ZrKzjyc8/n+zs+WRnn0pGRolugCEiMS2cmfupQIVz7kMAM1sKXAr0LvdLge+FPv8N8LCZmXPORTArAHv3PsjOnd/BOf+hmfaRmKWRkTGJ9PTJ5OcvIiNjMhkZJWRmziYzczYpKaMjHVFExHPhlPtEYG+vx5XAJw43xjnnN7MmoACo7T3IzG4Cbgo99JlZ+VBCD04XUBH66FchfXJGkWjNFq25IHqzRWsuiN5s0ZoLvM02OZxB4ZR7f+sTfWfk4YzBObcEWBLGzxwxZrbOOTff6xz9idZs0ZoLojdbtOaC6M0WrbkgurN9JCmMMZXAsb0eFwP7DzfGzFKAXKA+EgFFRGTwwin3tcAMM5tiZmnAYmBZnzHLgGtDn18BrBiO9XYREQnPgMsyoTX0rwKvEDwV8gnn3BYzuxtY55xbBvwCeMbMKgjO2BcPZ+gIi6ploj6iNVu05oLozRatuSB6s0VrLojubACYJtgiIvEnnGUZERGJMSp3EZE4lDDlbmYLzazczCrM7M7DjPl7M9tqZlvM7NloyWZmk8zsDTN718w2mdmiEcj0hJlVm9nmwzxvZvZQKPMmMzt5uDMNItvnQ5k2mdnbZnZitGTrNe4UM+sxsyuiJZeZnW1mG0K///89ErnCyWZmuWb2opltDGX74gjlOjb0vtsW+rlf62eMZ++DATnn4v6D4IHgHcBUIA3YCMzpM2YG8C4wJvR4bBRlWwJ8OfT5HGDXCOQ6EzgZ2HyY5xcBfyR4jcNpwJ9H8O9zoGxn9Pp7vDCasvX6O18BLAeuiIZcQB7Bq84nhR6PyO9/mNm+Dfw49HkRwZM20kYg13jg5NDn2cD7/bw3PXsfDPSRKDP3Q1soOOe6gI+2UOjtS8AjzrkGAOdcdRRlc0BO6PNcPn6dQcQ5597iyNcqXAo87YJWA3lmNn64c4WTzTn39kd/j8BqgtdmjIgw/rsB3AL8Fhip37Fwcn0OeN45tyc0PpqyOSDbghs+jQ6N9Y9Arirn3F9Cn7cA2whejd+bZ++DgSRKufe3hULfv6SZwEwz+5OZrQ7thBkt2b4HXG1mlQRne7eMTLQjCid3NLiB4MwqKpjZROAy4DGvs/QxExhjZm+a2Xozu8brQL08DMwmOKl5D/iacy4wkgHMrASYC/y5z1NR+z5IlP3cw9keIYXg0szZBGd6K83sBOdcYxRkuwp4yjl3v5mdTvCaghNG+he8j7C2nPCSmZ1DsNw/5XWWXh4E/tE51xNlO4+mENyy+zxgFPCOma12zr3vbSwA/g7YAJwLTANeNbOVzrnmkfjhZjaa4L+0buvnZ0bt+yBRyj3cLRRWO+e6gZ2hTc1mELxC1+tsNwALAZxz75hZBsGNi0bsn879CCe3Z8ysDHgcuNA5V+d1nl7mA0tDxV4ILDIzv3PuBW9jUQnUOudagVYzews4keA6s9e+CPzIBRe5K8xsJzALWDPcP9jMUgkW+386557vZ0jUvg8SZVkmnC0UXgDOATCzQoL/TP0wSrLtITijwsxmAxlAzQhkO5JlwDWhswVOA5qcc1UeZwKCZxcBzwNfiJKZ5yHOuSnOuRLnXAnB7bG/EgXFDvB7YIGZpZhZJsGdX7d5nOkjvX//xwHHMQLvzdAa/y+Abc65fzvMsKh9HyTEzN2Ft4XCK8AFZrYV6AG+ORIzvjCzfR34uZndTvCffNeFZjHDxsx+RXCJqjC01v9dIDWU+TGCa/+LCO6l3EZwdjUiwsh2F8Etp38WmiH73Qjt4BdGNk8MlMs5t83MXgarfjDeAAAAZklEQVQ2AQGCd1w74umcI5UN+AHwlJm9R3AZ5B+dcyOx3e4ngS8A75nZhtDXvg1M6pXNs/fBQLT9gIhIHEqUZRkRkYSichcRiUMqdxGROKRyFxGJQyp3EZE4pHIXEYlDKncRkTj0P5D+RlQW02VOAAAAAElFTkSuQmCC\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x2b865c5ccf28>"
                    }, 
                    "metadata": {}
                }
            ], 
            "source": "for step in range(2500):\n    _ , W_val,cost_val,b_val,hypor_val = sess.run([train,W,cost,b,hypothesis])\n    x.append(step)\n    y1.append(W_val)\n    y2.append(cost_val)\n    y3.append(b_val)\n    y4.append(hypor_val)\n    if step % 50 == 0:\n        print(step, sess.run(cost), sess.run(W), sess.run(b))\n#plt.plot(x,y1,c='r',label='W')\n#plt.plot(x,y2,c='b',label='cost')\n#plt.plot(x,y3,c='y',label='b')\n#plt.plot(x,y4,c='y',label='b')\nplt.plot(y1,y2,c='y',label='w-cost')\nplt.legend(shadow=True,fancybox=True,loc='upper right')\nplt.ylim(0,1)\nplt.show()   "
        }, 
        {
            "execution_count": 6, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x216711df5f8>"
                    }, 
                    "metadata": {}
                }, 
                {
                    "execution_count": 6, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "\"\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\n\\nX = np.arange(1,10,0.1)\\n#Y = [value + 2 for value in X]\\nY = [np.sin(value) for value in X]\\nZ = [np.cos(value) for value in X]\\n\\nfig = plt.figure()\\nfig.set_size_inches(10,5)\\n\\nplt.plot(X,Y,label='sin',linestyle='dotted')\\nplt.plot(X,Z,label='cos')\\nplt.title('$sine/cosine$')\\nplt.xlabel('$X_Label$')\\nplt.ylabel('Y_label')\\nplt.text(5,0,'sine')\\nplt.annotate('Co-sine',xy=(4,0.5),\\n             arrowprops=dict(facecolor='black',shrink=0.05),xytext=(2,0.5))\\nplt.legend(shadow=True,fancybox=True,loc='lower right')\\nplt.grid(True,ls='dotted',c='black',lw=0.5)\\n#plt.xlim(-10,6)\\n#plt.ylim(-2,20)\\nfig.savefig('test.jpg',c='a')\\nplt.show()          \\n\""
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "'''\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nX = np.arange(1,10,0.1)\n#Y = [value + 2 for value in X]\nY = [np.sin(value) for value in X]\nZ = [np.cos(value) for value in X]\n\nfig = plt.figure()\nfig.set_size_inches(10,5)\n\nplt.plot(X,Y,label='sin',linestyle='dotted')\nplt.plot(X,Z,label='cos')\nplt.title('$sine/cosine$')\nplt.xlabel('$X_Label$')\nplt.ylabel('Y_label')\nplt.text(5,0,'sine')\nplt.annotate('Co-sine',xy=(4,0.5),\n             arrowprops=dict(facecolor='black',shrink=0.05),xytext=(2,0.5))\nplt.legend(shadow=True,fancybox=True,loc='lower right')\nplt.grid(True,ls='dotted',c='black',lw=0.5)\n#plt.xlim(-10,6)\n#plt.ylim(-2,20)\nfig.savefig('test.jpg',c='a')\nplt.show()          \n'''"
        }
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