diff --git a/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Matplotlib.ipynb b/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Matplotlib.ipynb index 96f675e2..c1b8c742 100644 --- a/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Matplotlib.ipynb +++ b/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Matplotlib.ipynb @@ -1 +1,567 @@ -{"cells":[{"cell_type":"markdown","metadata":{"id":"_sfWZU_eST2O"},"source":["**NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP.**\n","\n","# Exercises\n","\n","Follow the instructions to recreate the plots using this data:\n","\n","## Data"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"l0eZ10dLST2X","executionInfo":{"status":"ok","timestamp":1765040320412,"user_tz":-330,"elapsed":18,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[],"source":["import numpy as np\n","x = np.arange(0,100)\n","y = x*2\n","z = x**2"]},{"cell_type":"markdown","metadata":{"id":"veY4-lsGST2c"},"source":["**Import matplotlib.pyplot as plt**"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"tAOmEcNKST2e","executionInfo":{"status":"ok","timestamp":1765040321909,"user_tz":-330,"elapsed":8,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[],"source":["import matplotlib.pyplot as plt"]},{"cell_type":"markdown","metadata":{"id":"5j_39gsVST2f"},"source":["## Exercise 1\n","\n","**Follow along with these steps**\n","* Create a figure object called fig using plt.figure()\n","* Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax.\n","* Plot (x,y) on that axes and set the labels and titles to match the plot below:"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"03Ts4r5SST2g","outputId":"3f0eaaad-30ab-4f95-be64-627648fefaaf","colab":{"base_uri":"https://localhost:8080/","height":599},"executionInfo":{"status":"ok","timestamp":1765040324606,"user_tz":-330,"elapsed":915,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'title')"]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["fig = plt.figure()\n","ax = fig.add_axes([0,0,1,1])\n","ax.plot(x,y)\n","ax.set_xlabel('x')\n","ax.set_ylabel('y')\n","ax.set_title('title')"]},{"cell_type":"markdown","metadata":{"id":"I8mFrIGwST2i"},"source":["## Exercise 2\n","**Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"D4nUfU26ST2k","outputId":"6e8b998b-bbf1-42de-afec-63e15dcccc63","colab":{"base_uri":"https://localhost:8080/","height":546},"executionInfo":{"status":"ok","timestamp":1765040364233,"user_tz":-330,"elapsed":313,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["fig = plt.figure()\n","ax1 = fig.add_axes([0,0,1,1])\n","ax2 = fig.add_axes([0.2,0.5,.2,.2])"]},{"cell_type":"markdown","metadata":{"id":"SK8p_A2NST2l"},"source":["**Now plot (x,y) on both axes. And call your figure object to show it.**"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"m_2gVMryST2m","outputId":"866ad5c1-11b6-49c4-a782-aad2fc2caa10","colab":{"base_uri":"https://localhost:8080/","height":540},"executionInfo":{"status":"ok","timestamp":1765040390662,"user_tz":-330,"elapsed":298,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["
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\n"},"metadata":{},"execution_count":6}],"source":["ax1.plot(x,y)\n","ax2.plot(x,y)\n","fig"]},{"cell_type":"markdown","metadata":{"id":"hYaQZfbFST2n"},"source":["## Exercise 3\n","\n","**Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"BuiYx0xbST2o","outputId":"0a15ceef-9d00-4e10-9e6f-10c8424789c9","colab":{"base_uri":"https://localhost:8080/","height":546},"executionInfo":{"status":"ok","timestamp":1765040412511,"user_tz":-330,"elapsed":257,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["fig = plt.figure()\n","ax1 = fig.add_axes([0,0,1,1])\n","ax2 = fig.add_axes([0.2,0.5,.4,.4])"]},{"cell_type":"markdown","metadata":{"id":"SeRSiZDGST2o"},"source":["**Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"jhy7t5AKST2p","outputId":"87a7ed37-08bf-4b4a-c976-1645d0bece85","colab":{"base_uri":"https://localhost:8080/","height":582},"executionInfo":{"status":"ok","timestamp":1765040431115,"user_tz":-330,"elapsed":314,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["
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\n"},"metadata":{},"execution_count":8}],"source":["ax1.plot(x,z)\n","ax1.set_xlabel('X')\n","ax1.set_ylabel('Z')\n","ax1.set_title('Larger Plot')\n","\n","ax2.plot(x,y)\n","ax2.set_xlabel('X')\n","ax2.set_ylabel('Y')\n","ax2.set_title('zoom')\n","ax2.set_xlim(20,22)\n","ax2.set_ylim(30,50)\n","\n","fig"]},{"cell_type":"markdown","metadata":{"id":"TM6XOILJST2q"},"source":["## Exercise 4\n","\n","**Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"]},{"cell_type":"code","execution_count":23,"metadata":{"id":"osk0Jco2ST2r","outputId":"7d090889-124c-4529-8ec5-836aef0a9033","colab":{"base_uri":"https://localhost:8080/","height":452},"executionInfo":{"status":"ok","timestamp":1765041091757,"user_tz":-330,"elapsed":171,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(
, array([, ], dtype=object))"]},"metadata":{},"execution_count":23},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}],"source":["plt.subplots(nrows=1, ncols=2)"]},{"cell_type":"markdown","metadata":{"id":"QWwItNZVST2r"},"source":["**Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"]},{"cell_type":"code","execution_count":26,"metadata":{"id":"ZoEMYyLOST2r","outputId":"522c4b25-4159-4cff-c793-8e579248e803","colab":{"base_uri":"https://localhost:8080/","height":487},"executionInfo":{"status":"ok","timestamp":1765041158259,"user_tz":-330,"elapsed":415,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["fig, axes = plt.subplots(nrows=1, ncols=2)\n","\n","axes[0].plot(x,y, color='blue', lw=3, ls='--')\n","axes[0].set_xlabel('X')\n","axes[0].set_ylabel('Y')\n","axes[0].set_title('X vs Y')\n","\n","axes[1].plot(x,z, color='red', lw=4, ls='-.')\n","axes[1].set_xlabel('X')\n","axes[1].set_ylabel('Z')\n","axes[1].set_title('X vs Z')\n","\n","plt.tight_layout()"]},{"cell_type":"markdown","metadata":{"id":"hV08IzIvST2s"},"source":["**See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"]},{"cell_type":"code","execution_count":28,"metadata":{"id":"0pq5vAFZST2s","outputId":"9a60b294-68c4-4012-fec5-da42ef1f4403","colab":{"base_uri":"https://localhost:8080/","height":407},"executionInfo":{"status":"ok","timestamp":1765041191581,"user_tz":-330,"elapsed":465,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\n","\n","axes[0].plot(x,y, color='blue', lw=3, ls='--')\n","axes[0].set_xlabel('X')\n","axes[0].set_ylabel('Y')\n","axes[0].set_title('X vs Y')\n","\n","axes[1].plot(x,z, color='red', lw=4, ls='-.')\n","axes[1].set_xlabel('X')\n","axes[1].set_ylabel('Z')\n","axes[1].set_title('X vs Z')\n","\n","plt.tight_layout()"]},{"cell_type":"markdown","metadata":{"id":"-ONbbrWEST2t"},"source":["# Great Job!"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.9.12"},"colab":{"provenance":[{"file_id":"1hYhHtDxal3ubd7qG8XFZDk8-XHoB_b1P","timestamp":1765040174190},{"file_id":"1TktwCMc95E3iZHIyOHJO0-h_UzQpVe4U","timestamp":1764745279737},{"file_id":"1tZurHlHqMzny5-rSxWhXrkRUpMckdLl5","timestamp":1731497437912}]}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_sfWZU_eST2O" + }, + "source": [ + "**NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP.**\n", + "\n", + "# Exercises\n", + "\n", + "Follow the instructions to recreate the plots using this data:\n", + "\n", + "## Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 18, + "status": "ok", + "timestamp": 1765040320412, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "l0eZ10dLST2X" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "x = np.arange(0,100)\n", + "y = x*2\n", + "\n", + "z = x**2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "veY4-lsGST2c" + }, + "source": [ + "**Import matplotlib.pyplot as plt**" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "executionInfo": { + "elapsed": 8, + "status": "ok", + "timestamp": 1765040321909, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "tAOmEcNKST2e" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5j_39gsVST2f" + }, + "source": [ + "## Exercise 1\n", + "\n", + "**Follow along with these steps**\n", + "* Create a figure object called fig using plt.figure()\n", + "* Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax.\n", + "* Plot (x,y) on that axes and set the labels and titles to match the plot below:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 599 + }, + "executionInfo": { + "elapsed": 915, + "status": "ok", + "timestamp": 1765040324606, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "03Ts4r5SST2g", + "outputId": "3f0eaaad-30ab-4f95-be64-627648fefaaf" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'title')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_axes([0,0,1,1])\n", + "ax.plot(x,y)\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "ax.set_title('title')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I8mFrIGwST2i" + }, + "source": [ + "## Exercise 2\n", + "**Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "executionInfo": { + "elapsed": 313, + "status": "ok", + "timestamp": 1765040364233, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "D4nUfU26ST2k", + "outputId": "6e8b998b-bbf1-42de-afec-63e15dcccc63" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax1 = fig.add_axes([0,0,1,1])\n", + "ax2 = fig.add_axes([0.2,0.5,.2,.2])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SK8p_A2NST2l" + }, + "source": [ + "**Now plot (x,y) on both axes. And call your figure object to show it.**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 540 + }, + "executionInfo": { + "elapsed": 298, + "status": "ok", + "timestamp": 1765040390662, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "m_2gVMryST2m", + "outputId": "866ad5c1-11b6-49c4-a782-aad2fc2caa10" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax1.plot(x,y)\n", + "ax2.plot(x,y)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hYaQZfbFST2n" + }, + "source": [ + "## Exercise 3\n", + "\n", + "**Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "executionInfo": { + "elapsed": 257, + "status": "ok", + "timestamp": 1765040412511, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "BuiYx0xbST2o", + "outputId": "0a15ceef-9d00-4e10-9e6f-10c8424789c9" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax1 = fig.add_axes([0,0,1,1])\n", + "ax2 = fig.add_axes([0.2,0.5,.4,.4])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SeRSiZDGST2o" + }, + "source": [ + "**Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 582 + }, + "executionInfo": { + "elapsed": 314, + "status": "ok", + "timestamp": 1765040431115, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "jhy7t5AKST2p", + "outputId": "87a7ed37-08bf-4b4a-c976-1645d0bece85" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax1.plot(x,z)\n", + "ax1.set_xlabel('X')\n", + "ax1.set_ylabel('Z')\n", + "ax1.set_title('Larger Plot')\n", + "\n", + "ax2.plot(x,y)\n", + "ax2.set_xlabel('X')\n", + "ax2.set_ylabel('Y')\n", + "ax2.set_title('zoom')\n", + "ax2.set_xlim(20,22)\n", + "ax2.set_ylim(30,50)\n", + "\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TM6XOILJST2q" + }, + "source": [ + "## Exercise 4\n", + "\n", + "**Use plt.subplots(nrows=1, ncols=2) to create the plot below.**" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "executionInfo": { + "elapsed": 171, + "status": "ok", + "timestamp": 1765041091757, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "osk0Jco2ST2r", + "outputId": "7d090889-124c-4529-8ec5-836aef0a9033" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(
, array([, ], dtype=object))" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(nrows=1, ncols=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QWwItNZVST2r" + }, + "source": [ + "**Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "executionInfo": { + "elapsed": 415, + "status": "ok", + "timestamp": 1765041158259, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "ZoEMYyLOST2r", + "outputId": "522c4b25-4159-4cff-c793-8e579248e803" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=2)\n", + "\n", + "axes[0].plot(x,y, color='blue', lw=3, ls='--')\n", + "axes[0].set_xlabel('X')\n", + "axes[0].set_ylabel('Y')\n", + "axes[0].set_title('X vs Y')\n", + "\n", + "axes[1].plot(x,z, color='red', lw=4, ls='-.')\n", + "axes[1].set_xlabel('X')\n", + "axes[1].set_ylabel('Z')\n", + "axes[1].set_title('X vs Z')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hV08IzIvST2s" + }, + "source": [ + "**See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 407 + }, + "executionInfo": { + "elapsed": 465, + "status": "ok", + "timestamp": 1765041191581, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "0pq5vAFZST2s", + "outputId": "9a60b294-68c4-4012-fec5-da42ef1f4403" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\n", + "\n", + "axes[0].plot(x,y, color='blue', lw=3, ls='--')\n", + "axes[0].set_xlabel('X')\n", + "axes[0].set_ylabel('Y')\n", + "axes[0].set_title('X vs Y')\n", + "\n", + "axes[1].plot(x,z, color='red', lw=4, ls='-.')\n", + "axes[1].set_xlabel('X')\n", + "axes[1].set_ylabel('Z')\n", + "axes[1].set_title('X vs Z')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-ONbbrWEST2t" + }, + "source": [ + "# Great Job!" + ] + } + ], + "metadata": { + "colab": { + "provenance": [ + { + "file_id": "1hYhHtDxal3ubd7qG8XFZDk8-XHoB_b1P", + "timestamp": 1765040174190 + }, + { + "file_id": "1TktwCMc95E3iZHIyOHJO0-h_UzQpVe4U", + "timestamp": 1764745279737 + }, + { + "file_id": "1tZurHlHqMzny5-rSxWhXrkRUpMckdLl5", + "timestamp": 1731497437912 + } + ] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Numpy .ipynb b/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Numpy .ipynb index 3a8061a9..5190d18b 100644 --- a/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Numpy .ipynb +++ b/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Numpy .ipynb @@ -1 +1,802 @@ -{"cells":[{"cell_type":"markdown","metadata":{"id":"HfJB9JnS9xlK"},"source":["# NumPy Exercises\n","\n","Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions."]},{"cell_type":"markdown","metadata":{"id":"u9POEVg29xlM"},"source":["#### Import NumPy as np"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"yyS-PuO_9xlM","executionInfo":{"status":"ok","timestamp":1765041749317,"user_tz":-330,"elapsed":10,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[],"source":["import numpy as np"]},{"cell_type":"markdown","metadata":{"id":"ehlpKUPc9xlM"},"source":["#### Create an array of 10 zeros"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"aEQkK-Dw9xlN","executionInfo":{"status":"ok","timestamp":1765041751898,"user_tz":-330,"elapsed":17,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[],"source":["arr = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])"]},{"cell_type":"markdown","metadata":{"id":"6QHp05Ei9xlN"},"source":["#### Create an array of 10 ones"]},{"cell_type":"code","execution_count":3,"metadata":{"scrolled":true,"id":"ebe9xrC29xlN","outputId":"9cd8dc76-73ee-4a0a-b674-a91a485307d7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041754087,"user_tz":-330,"elapsed":48,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[1 1 1 1 1 1 1 1 1]\n"]}],"source":["arr = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1])\n","print (arr)"]},{"cell_type":"markdown","metadata":{"id":"ziS-l3xp9xlO"},"source":["#### Create an array of 10 fives"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"fFC7bqLM9xlO","outputId":"775139e5-0c30-4875-bc33-a22b6c597390","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041756193,"user_tz":-330,"elapsed":8,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[5 5 5 5 5 5 5 5 5]\n"]}],"source":["arr = np.array([5, 5, 5, 5, 5, 5, 5, 5, 5])\n","print (arr)"]},{"cell_type":"markdown","metadata":{"id":"kBugMXlC9xlO"},"source":["#### Create an array of the integers from 10 to 50"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"JsO6qS9R9xlO","outputId":"ef90d24e-5d99-4c43-ab69-9a378e709587","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041758645,"user_tz":-330,"elapsed":22,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n"," 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50]\n"]}],"source":["arr = np.arange(10, 51)\n","print (arr)"]},{"cell_type":"markdown","metadata":{"id":"dMw4V2L79xlO"},"source":["#### Create an array of all the even integers from 10 to 50"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"4lK-5SQV9xlO","outputId":"d0a31fd5-0912-46ac-9854-74b4f82557a4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041760785,"user_tz":-330,"elapsed":12,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50]\n"]}],"source":["\n","arr = np.arange(10, 51, 2)\n","print (arr)"]},{"cell_type":"markdown","metadata":{"id":"g__F0rB39xlP"},"source":["#### Create a 3x3 matrix with values ranging from 0 to 8"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"MmVXCn0K9xlP","outputId":"85af3318-23bb-4988-8b32-5e321e8f9326","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041763179,"user_tz":-330,"elapsed":49,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[0 1 2]\n"," [3 4 5]\n"," [6 7 8]]\n"]}],"source":["arr = np.arange(9).reshape(3,3)\n","print (arr)"]},{"cell_type":"markdown","metadata":{"id":"4YfT0aLo9xlP"},"source":["#### Create a 3x3 identity matrix"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"UHa42UpQ9xlP","outputId":"b4df89be-3c81-4584-82e0-366c95b61565","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041764623,"user_tz":-330,"elapsed":23,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[1. 0. 0.]\n"," [0. 1. 0.]\n"," [0. 0. 1.]]\n"]}],"source":["arr = np.eye(3)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"bfwDbjhI9xlP"},"source":["#### Use NumPy to generate a random number between 0 and 1"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"Z0OroZxW9xlP","outputId":"51c5f3c5-92dc-4ab2-cf2e-5b54a1d9e790","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041766735,"user_tz":-330,"elapsed":28,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[0.91983048]\n"]}],"source":["arr = np.random.rand(1)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"rLW0Jjzp9xlP"},"source":["#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"szluy14n9xlP","outputId":"5ad01033-11e2-44b2-c13b-204493ee63e9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041769087,"user_tz":-330,"elapsed":20,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[-1.62106999 0.02698567 -0.82699824 1.87631527 0.27438302 0.55777336\n"," -0.25392969 0.15111265 -1.2319768 1.19935079 -0.3689512 -0.90231227\n"," 0.31347852 -1.41054963 -1.3254982 -1.01597887 0.46714617 0.49900407\n"," 1.11650707 -1.33980433 -2.29688123 -1.28781521 0.51107288 -0.13925542\n"," -0.13316229]\n"]}],"source":["arr = np.random.randn(25)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"_GhI8LYn9xlP"},"source":["#### Create the following matrix:"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"wS1ZBddV9xlP","outputId":"771e6bf0-8510-4bd2-b07a-86c3d81c01cb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041773206,"user_tz":-330,"elapsed":42,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 ]\n"," [0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2 ]\n"," [0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 0.3 ]\n"," [0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.4 ]\n"," [0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 ]\n"," [0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 0.6 ]\n"," [0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 ]\n"," [0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8 ]\n"," [0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 0.9 ]\n"," [0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1. ]]\n"]}],"source":["arr = np.arange(1, 101).reshape(10,10) / 100\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"3OqA-QtL9xlQ"},"source":["#### Create an array of 20 linearly spaced points between 0 and 1:"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"FNXTugQ29xlQ","outputId":"a2d567bb-1023-417a-c89c-e18ecd261a20","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041775593,"user_tz":-330,"elapsed":27,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[0. 0.05263158 0.10526316 0.15789474 0.21052632 0.26315789\n"," 0.31578947 0.36842105 0.42105263 0.47368421 0.52631579 0.57894737\n"," 0.63157895 0.68421053 0.73684211 0.78947368 0.84210526 0.89473684\n"," 0.94736842 1. ]\n"]}],"source":["arr = np.linspace(0, 1, 20)\n","print(arr)"]},{"cell_type":"markdown","metadata":{"id":"elx0EaxE9xlQ"},"source":["## Numpy Indexing and Selection\n","\n","Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"]},{"cell_type":"markdown","metadata":{"id":"2Tbm9kVf9xlQ"},"source":[]},{"cell_type":"code","execution_count":13,"metadata":{"id":"Ft8P8e249xlQ","outputId":"bc67c8b5-9d4e-4ddd-a380-53291f7aaae4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041781587,"user_tz":-330,"elapsed":27,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 1 2 3 4 5]\n"," [ 6 7 8 9 10]\n"," [11 12 13 14 15]\n"," [16 17 18 19 20]\n"," [21 22 23 24 25]]\n"]}],"source":["arr = np.arange(1, 26).reshape(5,5)\n","print(arr)"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"WrcFkpGL9xlQ","outputId":"0fe3a213-9068-4218-a178-175da43d476a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041784365,"user_tz":-330,"elapsed":20,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[12 13 14 15]\n"," [17 18 19 20]\n"," [22 23 24 25]]\n"]}],"source":["print(arr[2:, 1:])"]},{"cell_type":"code","execution_count":15,"metadata":{"id":"rnUSntEa9xlQ","outputId":"24856022-4bde-4dbd-e973-e8caddb85841","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041786999,"user_tz":-330,"elapsed":11,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["20\n"]}],"source":["print(arr[3, 4])"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"3DMBC_wp9xlQ","outputId":"19e51ddd-ec7f-42c3-8da9-f445384bab52","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041788639,"user_tz":-330,"elapsed":11,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 2]\n"," [ 7]\n"," [12]]\n"]}],"source":["print(arr[0:3, 1:2])"]},{"cell_type":"code","execution_count":17,"metadata":{"id":"pGjD4HUK9xlQ","outputId":"6352a0d3-1f55-47f6-c921-73a8ada29386","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041791254,"user_tz":-330,"elapsed":9,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[21 22 23 24 25]\n"]}],"source":["print(arr[4])"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"dqsdpxUo9xlR","outputId":"42ad7798-4a5a-4951-ccf9-9a196d745ff2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765041793966,"user_tz":-330,"elapsed":12,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[16 17 18 19 20]\n"," [21 22 23 24 25]]\n"]}],"source":["print(arr[3:])"]},{"cell_type":"markdown","metadata":{"collapsed":true,"id":"6vtHuVJU9xlf"},"source":["# Great Job!"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.9.12"},"colab":{"provenance":[{"file_id":"1uvwMRRqMFNlaYoWGT6JhFghskgPy9dzJ","timestamp":1765041644595},{"file_id":"1PacBskAxI7FPP4LIyB_3MysbL8ArUHsV","timestamp":1764745354238},{"file_id":"19ZajGBzqADvFnQ0kzbkad_udXeAh26pj","timestamp":1731497620277}]}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "HfJB9JnS9xlK" + }, + "source": [ + "# NumPy Exercises\n", + "\n", + "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u9POEVg29xlM" + }, + "source": [ + "#### Import NumPy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "executionInfo": { + "elapsed": 10, + "status": "ok", + "timestamp": 1765041749317, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "yyS-PuO_9xlM" + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ehlpKUPc9xlM" + }, + "source": [ + "#### Create an array of 10 zeros" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "executionInfo": { + "elapsed": 17, + "status": "ok", + "timestamp": 1765041751898, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "aEQkK-Dw9xlN" + }, + "outputs": [], + "source": [ + "arr = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6QHp05Ei9xlN" + }, + "source": [ + "#### Create an array of 10 ones" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 48, + "status": "ok", + "timestamp": 1765041754087, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "ebe9xrC29xlN", + "outputId": "9cd8dc76-73ee-4a0a-b674-a91a485307d7", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 1 1 1 1 1 1 1 1]\n" + ] + } + ], + "source": [ + "arr = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1])\n", + "\n", + "print (arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ziS-l3xp9xlO" + }, + "source": [ + "#### Create an array of 10 fives" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 8, + "status": "ok", + "timestamp": 1765041756193, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "fFC7bqLM9xlO", + "outputId": "775139e5-0c30-4875-bc33-a22b6c597390" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5 5 5 5 5 5 5 5 5]\n" + ] + } + ], + "source": [ + "arr = np.array([5, 5, 5, 5, 5, 5, 5, 5, 5])\n", + "print (arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kBugMXlC9xlO" + }, + "source": [ + "#### Create an array of the integers from 10 to 50" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 22, + "status": "ok", + "timestamp": 1765041758645, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "JsO6qS9R9xlO", + "outputId": "ef90d24e-5d99-4c43-ab69-9a378e709587" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n", + " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50]\n" + ] + } + ], + "source": [ + "arr = np.arange(10, 51)\n", + "print (arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dMw4V2L79xlO" + }, + "source": [ + "#### Create an array of all the even integers from 10 to 50" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 12, + "status": "ok", + "timestamp": 1765041760785, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "4lK-5SQV9xlO", + "outputId": "d0a31fd5-0912-46ac-9854-74b4f82557a4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50]\n" + ] + } + ], + "source": [ + "\n", + "arr = np.arange(10, 51, 2)\n", + "print (arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g__F0rB39xlP" + }, + "source": [ + "#### Create a 3x3 matrix with values ranging from 0 to 8" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 49, + "status": "ok", + "timestamp": 1765041763179, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "MmVXCn0K9xlP", + "outputId": "85af3318-23bb-4988-8b32-5e321e8f9326" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 1 2]\n", + " [3 4 5]\n", + " [6 7 8]]\n" + ] + } + ], + "source": [ + "arr = np.arange(9).reshape(3,3)\n", + "print (arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4YfT0aLo9xlP" + }, + "source": [ + "#### Create a 3x3 identity matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 23, + "status": "ok", + "timestamp": 1765041764623, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "UHa42UpQ9xlP", + "outputId": "b4df89be-3c81-4584-82e0-366c95b61565" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]]\n" + ] + } + ], + "source": [ + "arr = np.eye(3)\n", + "print(arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bfwDbjhI9xlP" + }, + "source": [ + "#### Use NumPy to generate a random number between 0 and 1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 28, + "status": "ok", + "timestamp": 1765041766735, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "Z0OroZxW9xlP", + "outputId": "51c5f3c5-92dc-4ab2-cf2e-5b54a1d9e790" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.91983048]\n" + ] + } + ], + "source": [ + "arr = np.random.rand(1)\n", + "print(arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rLW0Jjzp9xlP" + }, + "source": [ + "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 20, + "status": "ok", + "timestamp": 1765041769087, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "szluy14n9xlP", + "outputId": "5ad01033-11e2-44b2-c13b-204493ee63e9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-1.62106999 0.02698567 -0.82699824 1.87631527 0.27438302 0.55777336\n", + " -0.25392969 0.15111265 -1.2319768 1.19935079 -0.3689512 -0.90231227\n", + " 0.31347852 -1.41054963 -1.3254982 -1.01597887 0.46714617 0.49900407\n", + " 1.11650707 -1.33980433 -2.29688123 -1.28781521 0.51107288 -0.13925542\n", + " -0.13316229]\n" + ] + } + ], + "source": [ + "arr = np.random.randn(25)\n", + "print(arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_GhI8LYn9xlP" + }, + "source": [ + "#### Create the following matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 42, + "status": "ok", + "timestamp": 1765041773206, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "wS1ZBddV9xlP", + "outputId": "771e6bf0-8510-4bd2-b07a-86c3d81c01cb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 ]\n", + " [0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2 ]\n", + " [0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 0.3 ]\n", + " [0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.4 ]\n", + " [0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 ]\n", + " [0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 0.6 ]\n", + " [0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 ]\n", + " [0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8 ]\n", + " [0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 0.9 ]\n", + " [0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1. ]]\n" + ] + } + ], + "source": [ + "arr = np.arange(1, 101).reshape(10,10) / 100\n", + "print(arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3OqA-QtL9xlQ" + }, + "source": [ + "#### Create an array of 20 linearly spaced points between 0 and 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 27, + "status": "ok", + "timestamp": 1765041775593, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "FNXTugQ29xlQ", + "outputId": "a2d567bb-1023-417a-c89c-e18ecd261a20" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0.05263158 0.10526316 0.15789474 0.21052632 0.26315789\n", + " 0.31578947 0.36842105 0.42105263 0.47368421 0.52631579 0.57894737\n", + " 0.63157895 0.68421053 0.73684211 0.78947368 0.84210526 0.89473684\n", + " 0.94736842 1. ]\n" + ] + } + ], + "source": [ + "arr = np.linspace(0, 1, 20)\n", + "print(arr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "elx0EaxE9xlQ" + }, + "source": [ + "## Numpy Indexing and Selection\n", + "\n", + "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2Tbm9kVf9xlQ" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 27, + "status": "ok", + "timestamp": 1765041781587, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "Ft8P8e249xlQ", + "outputId": "bc67c8b5-9d4e-4ddd-a380-53291f7aaae4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1 2 3 4 5]\n", + " [ 6 7 8 9 10]\n", + " [11 12 13 14 15]\n", + " [16 17 18 19 20]\n", + " [21 22 23 24 25]]\n" + ] + } + ], + "source": [ + "arr = np.arange(1, 26).reshape(5,5)\n", + "print(arr)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 20, + "status": "ok", + "timestamp": 1765041784365, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "WrcFkpGL9xlQ", + "outputId": "0fe3a213-9068-4218-a178-175da43d476a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[12 13 14 15]\n", + " [17 18 19 20]\n", + " [22 23 24 25]]\n" + ] + } + ], + "source": [ + "print(arr[2:, 1:])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 11, + "status": "ok", + "timestamp": 1765041786999, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "rnUSntEa9xlQ", + "outputId": "24856022-4bde-4dbd-e973-e8caddb85841" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(arr[3, 4])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 11, + "status": "ok", + "timestamp": 1765041788639, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "3DMBC_wp9xlQ", + "outputId": "19e51ddd-ec7f-42c3-8da9-f445384bab52" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2]\n", + " [ 7]\n", + " [12]]\n" + ] + } + ], + "source": [ + "print(arr[0:3, 1:2])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 9, + "status": "ok", + "timestamp": 1765041791254, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "pGjD4HUK9xlQ", + "outputId": "6352a0d3-1f55-47f6-c921-73a8ada29386" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[21 22 23 24 25]\n" + ] + } + ], + "source": [ + "print(arr[4])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 12, + "status": "ok", + "timestamp": 1765041793966, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "dqsdpxUo9xlR", + "outputId": "42ad7798-4a5a-4951-ccf9-9a196d745ff2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[16 17 18 19 20]\n", + " [21 22 23 24 25]]\n" + ] + } + ], + "source": [ + "print(arr[3:])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "id": "6vtHuVJU9xlf" + }, + "source": [ + "# Great Job!" + ] + } + ], + "metadata": { + "colab": { + "provenance": [ + { + "file_id": "1uvwMRRqMFNlaYoWGT6JhFghskgPy9dzJ", + "timestamp": 1765041644595 + }, + { + "file_id": "1PacBskAxI7FPP4LIyB_3MysbL8ArUHsV", + "timestamp": 1764745354238 + }, + { + "file_id": "19ZajGBzqADvFnQ0kzbkad_udXeAh26pj", + "timestamp": 1731497620277 + } + ] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Panda.ipynb b/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Panda.ipynb index 0fbf0d86..d45a5e5b 100644 --- a/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Panda.ipynb +++ b/Assignment 1/Assignment1_rudrakumar/rudrakumar_Assignment1_Panda.ipynb @@ -1 +1,3358 @@ -{"cells":[{"cell_type":"markdown","source":["## General Instructions for all Assignments"],"metadata":{"id":"AWDeauE3Bdzs"}},{"cell_type":"markdown","source":["1. Fork the repository and make a copy of this notebook and rename it as your name_Assignment1_Pandas\n","2. Generate a pull request. The pull request message should also be yourname_Assignment1"],"metadata":{"id":"Xaf9uMvXBJEC"}},{"cell_type":"markdown","source":["\n","1. This code notebook is composed of cells. Each cell is either text or Python code.\n","2. To run a cell of code, click the \"play button\" icon to the left of the cell or click on the cell and press \"Shift+Enter\" on your keyboard. This will execute the Python code contained in the cell. Executing a cell that defines a variable is important before executing or authoring a cell that depends on that previously created variable assignment.\n"],"metadata":{"id":"4_6uxHVGB_XT"}},{"cell_type":"markdown","source":[],"metadata":{"id":"b68xNjkKCBIF"}},{"cell_type":"code","execution_count":7,"metadata":{"id":"BiD4gl6_cAYs","executionInfo":{"status":"ok","timestamp":1765038338466,"user_tz":-330,"elapsed":9,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[],"source":["#import pandas library\n","import pandas as pd"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"JUtm9KricAYu","executionInfo":{"status":"ok","timestamp":1765038342539,"user_tz":-330,"elapsed":36,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[],"source":["df = pd.read_csv('Iris.csv') #enter path for the Iris csv in the brackets\n","#reads the csv file and stores it in a data frame(define in the pandas library)"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"qzUnSkfRcAYu","executionInfo":{"status":"ok","timestamp":1765038344404,"user_tz":-330,"elapsed":9,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[],"source":["dictionary = {'col':[4,2,3], 'col2':[4,7,6], 'col3':[7,10,9]}"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"ACswu4BXcAYu","executionInfo":{"status":"ok","timestamp":1765038346628,"user_tz":-330,"elapsed":11,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}}},"outputs":[],"source":["dictionarydf = pd.DataFrame.from_dict(dictionary)\n","#it creats a new datafram which stores the dictionary made above"]},{"cell_type":"code","source":["df.head()\n","#shows top 5 rows (row indexing starts from 0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"MvYNz8E2dp3K","executionInfo":{"status":"ok","timestamp":1765038349347,"user_tz":-330,"elapsed":70,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}},"outputId":"45be8697-fd82-4a2d-93f5-e1606460279d"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n","0 1 5.1 3.5 1.4 0.2 Iris-setosa\n","1 2 4.9 3.0 1.4 0.2 Iris-setosa\n","2 3 4.7 3.2 1.3 0.2 Iris-setosa\n","3 4 4.6 3.1 1.5 0.2 Iris-setosa\n","4 5 5.0 3.6 1.4 0.2 Iris-setosa"],"text/html":["\n","
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IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
015.13.51.40.2Iris-setosa
124.93.01.40.2Iris-setosa
234.73.21.30.2Iris-setosa
344.63.11.50.2Iris-setosa
455.03.61.40.2Iris-setosa
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \"#shows top 5 rows (row indexing starts from 0)\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"Id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 5,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SepalLengthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2073644135332772,\n \"min\": 4.6,\n \"max\": 5.1,\n \"num_unique_values\": 5,\n \"samples\": [\n 4.9,\n 5.0,\n 4.7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SepalWidthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2588435821108957,\n \"min\": 3.0,\n \"max\": 3.6,\n \"num_unique_values\": 5,\n \"samples\": [\n 3.0,\n 3.6,\n 3.2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PetalLengthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07071067811865474,\n \"min\": 1.3,\n \"max\": 1.5,\n \"num_unique_values\": 3,\n \"samples\": [\n 1.4,\n 1.3,\n 1.5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PetalWidthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.2,\n \"max\": 0.2,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Species\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Iris-setosa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":11}]},{"cell_type":"code","execution_count":13,"metadata":{"id":"iAx3B-TacAYu","colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"status":"ok","timestamp":1765038353697,"user_tz":-330,"elapsed":10,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}},"outputId":"f71b39c3-82fa-4341-a7e0-da4ec309f504"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n","0 1 5.1 3.5 1.4 0.2 Iris-setosa\n","1 2 4.9 3.0 1.4 0.2 Iris-setosa\n","2 3 4.7 3.2 1.3 0.2 Iris-setosa\n","3 4 4.6 3.1 1.5 0.2 Iris-setosa\n","4 5 5.0 3.6 1.4 0.2 Iris-setosa\n","5 6 5.4 3.9 1.7 0.4 Iris-setosa\n","6 7 4.6 3.4 1.4 0.3 Iris-setosa\n","7 8 5.0 3.4 1.5 0.2 Iris-setosa\n","8 9 4.4 2.9 1.4 0.2 Iris-setosa\n","9 10 4.9 3.1 1.5 0.1 Iris-setosa"],"text/html":["\n","
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IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
015.13.51.40.2Iris-setosa
124.93.01.40.2Iris-setosa
234.73.21.30.2Iris-setosa
344.63.11.50.2Iris-setosa
455.03.61.40.2Iris-setosa
565.43.91.70.4Iris-setosa
674.63.41.40.3Iris-setosa
785.03.41.50.2Iris-setosa
894.42.91.40.2Iris-setosa
9104.93.11.50.1Iris-setosa
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IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
1351367.73.06.12.3Iris-virginica
1361376.33.45.62.4Iris-virginica
1371386.43.15.51.8Iris-virginica
1381396.03.04.81.8Iris-virginica
1391406.93.15.42.1Iris-virginica
1401416.73.15.62.4Iris-virginica
1411426.93.15.12.3Iris-virginica
1421435.82.75.11.9Iris-virginica
1431446.83.25.92.3Iris-virginica
1441456.73.35.72.5Iris-virginica
1451466.73.05.22.3Iris-virginica
1461476.32.55.01.9Iris-virginica
1471486.53.05.22.0Iris-virginica
1481496.23.45.42.3Iris-virginica
1491505.93.05.11.8Iris-virginica
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0
Idint64
SepalLengthCmfloat64
SepalWidthCmfloat64
PetalLengthCmfloat64
PetalWidthCmfloat64
Speciesobject
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"]},"metadata":{},"execution_count":18}],"source":["#show the data types of each feature or column name\n","df.dtypes"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"lT3OEUNDcAYv","colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"status":"ok","timestamp":1765038419353,"user_tz":-330,"elapsed":28,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}},"outputId":"0bb6493e-3fd8-464d-b579-ed93058965d0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm\n","count 150.000000 150.000000 150.000000 150.000000 150.000000\n","mean 75.500000 5.843333 3.054000 3.758667 1.198667\n","std 43.445368 0.828066 0.433594 1.764420 0.763161\n","min 1.000000 4.300000 2.000000 1.000000 0.100000\n","25% 38.250000 5.100000 2.800000 1.600000 0.300000\n","50% 75.500000 5.800000 3.000000 4.350000 1.300000\n","75% 112.750000 6.400000 3.300000 5.100000 1.800000\n","max 150.000000 7.900000 4.400000 6.900000 2.500000"],"text/html":["\n","
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IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCm
count150.000000150.000000150.000000150.000000150.000000
mean75.5000005.8433333.0540003.7586671.198667
std43.4453680.8280660.4335941.7644200.763161
min1.0000004.3000002.0000001.0000000.100000
25%38.2500005.1000002.8000001.6000000.300000
50%75.5000005.8000003.0000004.3500001.300000
75%112.7500006.4000003.3000005.1000001.800000
max150.0000007.9000004.4000006.9000002.500000
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SepalLengthCmSepalWidthCm
05.13.5
14.93.0
24.73.2
34.63.1
45.03.6
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1456.73.0
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150 rows × 2 columns

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10
Id11
SepalLengthCm5.4
SepalWidthCm3.7
PetalLengthCm1.5
PetalWidthCm0.2
SpeciesIris-setosa
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"]},"metadata":{},"execution_count":22}],"source":["#Get the 11th row from the dataset\n","#Hint: for taking 11th row do we use 11 or 10 index?\n","df.iloc[10]"]},{"cell_type":"code","source":["df.iloc[10,-1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"TF03jh_MovET","executionInfo":{"status":"ok","timestamp":1765038578987,"user_tz":-330,"elapsed":16,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}},"outputId":"7a36d327-16bf-48ed-c72e-ce2119d2e364"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Iris-setosa'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["#Display the second last entry of the 10th row\n","df.iloc[9,-2]"],"metadata":{"id":"gV-h3rK-g5lZ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1765038601112,"user_tz":-330,"elapsed":28,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}},"outputId":"8945c106-46b4-4ea2-d0a3-49a1add4d969"},"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["np.float64(0.1)"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"KXWCH34ecAYx","executionInfo":{"status":"ok","timestamp":1765038616280,"user_tz":-330,"elapsed":21,"user":{"displayName":"Rudra kumar","userId":"13161532119588205553"}},"outputId":"0b924f8c-38d9-4637-9f15-25fb483b6469"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n","22 23 4.6 3.6 1.0 0.2 Iris-setosa\n","23 24 5.1 3.3 1.7 0.5 Iris-setosa\n","24 25 4.8 3.4 1.9 0.2 Iris-setosa\n","25 26 5.0 3.0 1.6 0.2 Iris-setosa\n","26 27 5.0 3.4 1.6 0.4 Iris-setosa\n","27 28 5.2 3.5 1.5 0.2 Iris-setosa\n","28 29 5.2 3.4 1.4 0.2 Iris-setosa\n","29 30 4.7 3.2 1.6 0.2 Iris-setosa\n","30 31 4.8 3.1 1.6 0.2 Iris-setosa\n","31 32 5.4 3.4 1.5 0.4 Iris-setosa\n","32 33 5.2 4.1 1.5 0.1 Iris-setosa"],"text/html":["\n","
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IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
22234.63.61.00.2Iris-setosa
23245.13.31.70.5Iris-setosa
24254.83.41.90.2Iris-setosa
25265.03.01.60.2Iris-setosa
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Fork the repository and make a copy of this notebook and rename it as your name_Assignment1_Pandas\n", + "2. Generate a pull request. The pull request message should also be yourname_Assignment1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4_6uxHVGB_XT" + }, + "source": [ + "\n", + "1. This code notebook is composed of cells. Each cell is either text or Python code.\n", + "2. To run a cell of code, click the \"play button\" icon to the left of the cell or click on the cell and press \"Shift+Enter\" on your keyboard. This will execute the Python code contained in the cell. Executing a cell that defines a variable is important before executing or authoring a cell that depends on that previously created variable assignment.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b68xNjkKCBIF" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 9, + "status": "ok", + "timestamp": 1765038338466, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "BiD4gl6_cAYs" + }, + "outputs": [], + "source": [ + "#import pandas library\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "executionInfo": { + "elapsed": 36, + "status": "ok", + "timestamp": 1765038342539, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "JUtm9KricAYu" + }, + "outputs": [], + "source": [ + "df = pd.read_csv('Iris.csv') #enter path for the Iris csv in the brackets\n", + "#reads the csv file and stores it in a data frame(define in the pandas library)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "executionInfo": { + "elapsed": 9, + "status": "ok", + "timestamp": 1765038344404, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "qzUnSkfRcAYu" + }, + "outputs": [], + "source": [ + "dictionary = {'col':[4,2,3], 'col2':[4,7,6], 'col3':[7,10,9]}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "executionInfo": { + "elapsed": 11, + "status": "ok", + "timestamp": 1765038346628, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "ACswu4BXcAYu" + }, + "outputs": [], + "source": [ + "dictionarydf = pd.DataFrame.from_dict(dictionary)\n", + "#it creats a new datafram which stores the dictionary made above" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "executionInfo": { + "elapsed": 70, + "status": "ok", + "timestamp": 1765038349347, + "user": { + "displayName": "Rudra kumar", + "userId": "13161532119588205553" + }, + "user_tz": -330 + }, + "id": "MvYNz8E2dp3K", + "outputId": "45be8697-fd82-4a2d-93f5-e1606460279d" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"#shows top 5 rows (row indexing starts from 0)\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"Id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 5,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SepalLengthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2073644135332772,\n \"min\": 4.6,\n \"max\": 5.1,\n \"num_unique_values\": 5,\n \"samples\": [\n 4.9,\n 5.0,\n 4.7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SepalWidthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2588435821108957,\n \"min\": 3.0,\n \"max\": 3.6,\n \"num_unique_values\": 5,\n \"samples\": [\n 3.0,\n 3.6,\n 3.2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PetalLengthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07071067811865474,\n \"min\": 1.3,\n \"max\": 1.5,\n \"num_unique_values\": 3,\n \"samples\": [\n 1.4,\n 1.3,\n 1.5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PetalWidthCm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.2,\n \"max\": 0.2,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Species\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Iris-setosa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, + "text/html": [ + "\n", + "
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IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
22234.63.61.00.2Iris-setosa
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26275.03.41.60.4Iris-setosa
27285.23.51.50.2Iris-setosa
28295.23.41.40.2Iris-setosa
29304.73.21.60.2Iris-setosa
30314.83.11.60.2Iris-setosa
31325.43.41.50.4Iris-setosa
32335.24.11.50.1Iris-setosa
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IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
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1451466.73.05.22.3Iris-virginica
1461476.32.55.01.9Iris-virginica
1471486.53.05.22.0Iris-virginica
1481496.23.45.42.3Iris-virginica
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} + ], + "metadata": { + "colab": { + "provenance": [ + { + "file_id": "1oHWA18alfboCoMUxg9m8jHe3jh_QZc7-", + "timestamp": 1765038905121 + }, + { + "file_id": "1tVNRnxnutLstrGKY3P0fbmpEVSwXiDnJ", + "timestamp": 1765037909887 + }, + { + "file_id": "1PJDPRd4uOSxvoSK0o_O22PiQ2-AkDis4", + "timestamp": 1764744257366 + }, + { + "file_id": "1AjoKNd7ieBCZ4NDPgzWDLDMsHKuYsb5A", + "timestamp": 1731497483327 + } + ] + }, + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Assignment 3/Assignment3_rudrakumar/dataset.zip b/Assignment 3/Assignment3_rudrakumar/dataset.zip new file mode 100644 index 00000000..bd858c05 Binary files /dev/null and b/Assignment 3/Assignment3_rudrakumar/dataset.zip differ diff --git a/Assignment 3/Assignment3_rudrakumar/requirements.txt b/Assignment 3/Assignment3_rudrakumar/requirements.txt new file mode 100644 index 00000000..16534564 --- /dev/null +++ b/Assignment 3/Assignment3_rudrakumar/requirements.txt @@ -0,0 +1,6 @@ +tensorflow +numpy +matplotlib +scikit-learn +panda +zipfile \ No newline at end of file diff --git a/Assignment 3/Assignment3_rudrakumar/rudrakumar_Assignment3.py b/Assignment 3/Assignment3_rudrakumar/rudrakumar_Assignment3.py new file mode 100644 index 00000000..fe9c1980 --- /dev/null +++ b/Assignment 3/Assignment3_rudrakumar/rudrakumar_Assignment3.py @@ -0,0 +1,102 @@ +#library import + +import tensorflow as tf +from tensorflow.keras.preprocessing.image import ImageDataGenerator +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout +from sklearn.metrics import confusion_matrix +import numpy as np +import matplotlib.pyplot as plt +from zipfile import ZipFile + +#extracted zip for dataset + +zip_path = 'dataset.zip' +extract_path = 'dataset/' +with ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(extract_path) + +#data preparation + +trainpath = 'dataset/Train' +testpath = 'dataset/Test' + +imgsize = 160 +batchsize = 32 +epoch = 25 + +traindata = ImageDataGenerator(rescale=1./255, + rotation_range=15, + zoom_range=0.15, + width_shift_range=0.1, + height_shift_range=0.1, + horizontal_flip=True).flow_from_directory(trainpath, + target_size=(imgsize, imgsize), + batch_size=batchsize, + class_mode='binary') + +testdata = ImageDataGenerator(rescale=1./255).flow_from_directory(testpath, + target_size=(imgsize, imgsize), + batch_size=batchsize, + class_mode='binary', + shuffle=False) + +#model creation + +model = Sequential([ + Conv2D(32, (3, 3), activation='relu', input_shape=(imgsize, imgsize, 3)), + MaxPooling2D(2, 2), + Conv2D(64, (3, 3), activation='relu'), + MaxPooling2D(2, 2), + Conv2D(128, (3, 3), activation='relu'), + MaxPooling2D(2, 2), + Flatten(), + Dense(128, activation='relu'), + Dropout(0.5), + Dense(64, activation='relu'), + Dropout(0.3), + Dense(1, activation='sigmoid') +]) + +model.compile( + optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005), + loss='binary_crossentropy', + metrics=['accuracy'] +) + +#model training + +history = model.fit( + traindata, + validation_data=testdata, + epochs=epoch, + verbose=1 +) + +#plot wala part + +plt.plot(history.history['accuracy'], label='train accuracy') +plt.plot(history.history['val_accuracy'], label='test accuracy') +plt.title('Model Accuracy') +plt.legend(['Train', 'Test']) +plt.show() + +plt.plot(history.history['loss'], label='train loss') +plt.plot(history.history['val_loss'], label='test loss') +plt.title('Model Loss') +plt.legend(['Train', 'Test']) +plt.show() + +y_pred_prob = model.predict(testdata, verbose=1) +y_pred = (y_pred_prob > 0.5).astype(int) +y_true = testdata.classes +cm = confusion_matrix(y_true, y_pred) +plt.imshow(cm, cmap="Blues") +plt.title("Confusion Matrix") +plt.colorbar() +plt.xlabel("Predicted Labels") +plt.ylabel("True Labels") +for i in range (2): + for j in range (2): + plt.text(j, i, cm[i, j], ha='center', va='center') +plt.show() \ No newline at end of file diff --git a/Assignment 4/Assignment4_rudrakumar/requirements.txt b/Assignment 4/Assignment4_rudrakumar/requirements.txt new file mode 100644 index 00000000..5489c279 --- /dev/null +++ b/Assignment 4/Assignment4_rudrakumar/requirements.txt @@ -0,0 +1,8 @@ +numpy +pandas +yfinance +mplfinance +tensorflow +scikit-learn +matplotlib +seaborn \ No newline at end of file diff --git a/Assignment 4/Assignment4_rudrakumar/rudrakumar_Assignment4.py b/Assignment 4/Assignment4_rudrakumar/rudrakumar_Assignment4.py new file mode 100644 index 00000000..ac7436db --- /dev/null +++ b/Assignment 4/Assignment4_rudrakumar/rudrakumar_Assignment4.py @@ -0,0 +1,355 @@ +import os +import random +import numpy as np +import pandas as pd +import yfinance as yf +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import tensorflow as tf +from tensorflow.keras import layers, models +from tensorflow.keras.preprocessing.image import ImageDataGenerator +from tensorflow.keras.utils import Sequence, to_categorical, load_img, img_to_array +from sklearn.model_selection import train_test_split +from sklearn.metrics import confusion_matrix + +# CONFIG +SIZE = 64 +EPOCHS = 100 +BATCH = 32 + +stocks = ['AAPL','GOOGL','MSFT','AMZN','TSLA','NVDA','AMD','INTC','NFLX','META'] +train_folder = "images_train" +test_folder = "images_test" + +np.random.seed(42) +tf.random.set_seed(42) + +# FOLDERS +for f in [train_folder, test_folder]: + for lbl in ['Hammer','Doji','None']: + os.makedirs(os.path.join(f, lbl), exist_ok=True) + +# PATTERN LOGIC +def check_pattern(row): + body = abs(row['Open'] - row['Close']) + lower = min(row['Open'], row['Close']) - row['Low'] + upper = row['High'] - max(row['Open'], row['Close']) + total = row['High'] - row['Low'] + if total == 0: + return 'None' + if lower >= 2 * body and upper <= 0.5 * body: + return 'Hammer' + if body <= 0.1 * total: + return 'Doji' + return 'None' + +# IMAGE DRAW +def draw_candle(data, path): + fig, ax = plt.subplots(figsize=(1,1)) + ax.axis('off') + + mn, mx = data['Low'].min(), data['High'].max() + norm = lambda x: (x - mn) / (mx - mn + 1e-6) + + for i, r in data.iterrows(): + color = 'green' if r['Close'] >= r['Open'] else 'red' + ax.plot([i,i],[norm(r['Low']),norm(r['High'])],color='black',lw=1) + op, cl = norm(r['Open']), norm(r['Close']) + rect = plt.Rectangle((i-0.3,min(op,cl)),0.6, + max(abs(cl-op),0.02),color=color) + ax.add_patch(rect) + + plt.savefig(path, dpi=64, bbox_inches='tight', pad_inches=0) + plt.close(fig) + +# DATA CREATION +def build_data(start, end, folder, is_train=True): + raw = yf.download(stocks, start=start, end=end, + group_by='ticker', progress=False) + rows = [] + + for s in stocks: + df = raw[s].dropna().reset_index(drop=True) + if df.empty: + continue + + df['Pattern'] = df.apply(check_pattern, axis=1) + df['Tomorrow'] = df['Close'].shift(-1) - df['Close'] + df['Direction'] = np.where(df['Tomorrow'] > 0, 'Up', 'Down') + + for i in range(20, len(df)-1): + p = df.loc[i,'Pattern'] + if is_train and p == 'None' and random.random() > 0.15: + continue + + fname = f"{s}_{i}_{p}.png" + path = os.path.join(folder, p, fname) + + if not os.path.exists(path): + draw_candle(df.iloc[i-9:i+1].reset_index(drop=True), path) + + rows.append({ + 'path': path, + 'pattern': p, + 'direction': df.loc[i,'Direction'], + 'ret': df.loc[i,'Tomorrow'] + }) + + return pd.DataFrame(rows) + +# BUILD DATA +train_df = build_data('2018-01-01','2023-12-31',train_folder,True).reset_index(drop=True) +test_df = build_data('2024-01-01','2024-06-01',test_folder,False).reset_index(drop=True) + +train_df, val_df = train_test_split( + train_df, + test_size=0.2, + stratify=train_df['pattern'], + random_state=42 +) + +# IMAGE GENERATORS +pattern_classes = ['Hammer', 'Doji', 'None'] +direction_classes = ['Up', 'Down'] +pattern_to_idx = {c: i for i, c in enumerate(pattern_classes)} +direction_to_idx = {c: i for i, c in enumerate(direction_classes)} + +train_gen = ImageDataGenerator( + rescale=1./255, + rotation_range=10, + zoom_range=0.1, + width_shift_range=0.1, + height_shift_range=0.1, + brightness_range=(0.7,1.3) +) + +val_gen = ImageDataGenerator(rescale=1./255) + +class TradingSequence(Sequence): + def __init__(self, df, batch_size, datagen=None, shuffle=True): + self.df = df.reset_index(drop=True) + self.batch_size = batch_size + self.datagen = datagen + self.shuffle = shuffle + self.indices = np.arange(len(self.df)) + self.on_epoch_end() + + self.pat_idx = self.df['pattern'].map(pattern_to_idx).values + self.dir_idx = self.df['direction'].map(direction_to_idx).values + + def __len__(self): + return int(np.ceil(len(self.df) / self.batch_size)) + + def __getitem__(self, index): + batch_idx = self.indices[index * self.batch_size:(index + 1) * self.batch_size] + batch_paths = self.df.loc[batch_idx, 'path'].values + + x = np.zeros((len(batch_paths), SIZE, SIZE, 3), dtype=np.float32) + for i, p in enumerate(batch_paths): + img = load_img(p, target_size=(SIZE, SIZE)) + arr = img_to_array(img) + if self.datagen is not None: + arr = self.datagen.random_transform(arr) + arr = self.datagen.standardize(arr) + else: + arr = arr / 255.0 + x[i] = arr + + y_pat = to_categorical(self.pat_idx[batch_idx], num_classes=len(pattern_classes)) + y_dir = to_categorical(self.dir_idx[batch_idx], num_classes=len(direction_classes)) + + return x, {'pattern': y_pat, 'direction': y_dir} + + def on_epoch_end(self): + if self.shuffle: + np.random.shuffle(self.indices) + +train_run = TradingSequence(train_df, BATCH, datagen=train_gen, shuffle=True) +val_run = TradingSequence(val_df, BATCH, datagen=val_gen, shuffle=False) + +# MODEL +inp = layers.Input(shape=(SIZE,SIZE,3)) + +x = layers.Conv2D(32,(3,3),padding='same',activation='relu')(inp) +x = layers.BatchNormalization()(x) +x = layers.MaxPooling2D()(x) + +x = layers.Conv2D(64,(3,3),padding='same',activation='relu')(x) +x = layers.BatchNormalization()(x) +x = layers.MaxPooling2D()(x) + +x = layers.Conv2D(128,(3,3),padding='same',activation='relu')(x) +x = layers.BatchNormalization()(x) + +x = layers.GlobalAveragePooling2D()(x) +x = layers.Dense(64,activation='relu', + kernel_regularizer=tf.keras.regularizers.l2(1e-4))(x) +x = layers.Dropout(0.5)(x) + +pattern_out = layers.Dense(3,activation='softmax',name='pattern')(x) +direction_out = layers.Dense(2,activation='softmax',name='direction')(x) + +model = models.Model(inp, [pattern_out, direction_out]) + +model.compile( + optimizer=tf.keras.optimizers.Adam(1e-3), + loss={ + 'pattern': tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1), + 'direction': tf.keras.losses.CategoricalCrossentropy() + }, + loss_weights={'pattern':1.0,'direction':0.5}, + metrics={'pattern':'accuracy','direction':'accuracy'} +) + +# TRAIN +history = model.fit( + train_run, + validation_data=val_run, + epochs=EPOCHS, + verbose=1 +) + +# TEST EVALUATION +test_run = TradingSequence(test_df, BATCH, datagen=val_gen, shuffle=False) +pat_pred, dir_pred = model.predict(test_run) + +pat_cls = np.argmax(pat_pred, axis=1) +dir_cls = np.argmax(dir_pred, axis=1) + +hammer_idx = pattern_to_idx['Hammer'] +up_idx = direction_to_idx['Up'] + +pnl = [] +wins = losses = 0 + +for i,row in test_df.iterrows(): + if pat_cls[i] == hammer_idx and dir_cls[i] == up_idx: + pnl.append(row['ret']) + wins += row['ret'] > 0 + losses += row['ret'] <= 0 + +# RESULTS +print("\n"+"="*40) +print("JOINT LEARNING TRADING RESULTS") +print("="*40) + +if wins + losses: + print(f"Trades : {wins + losses}") + print(f"Win Rate : {(wins/(wins+losses))*100:.2f}%") + print(f"Total PnL : {sum(pnl):.2f}") +else: + print("No trades executed") + +print("-"*40) +print(f"Final Train Pattern Acc : {history.history['pattern_accuracy'][-1]*100:.1f}%") +print(f"Final Val Pattern Acc : {history.history['val_pattern_accuracy'][-1]*100:.1f}%") +print("="*40) + +# VISUALIZATIONS +import itertools + +results_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'results') +os.makedirs(results_dir, exist_ok=True) + +# 1) Training history: pattern accuracy & loss +try: + plt.figure(figsize=(6,4)) + plt.plot(history.history.get('pattern_accuracy', []), label='train_pattern_acc') + plt.plot(history.history.get('val_pattern_accuracy', []), label='val_pattern_acc') + if 'direction_accuracy' in history.history: + plt.plot(history.history.get('direction_accuracy', []), '--', label='train_direction_acc') + plt.plot(history.history.get('val_direction_accuracy', []), '--', label='val_direction_acc') + plt.xlabel('Epoch') + plt.ylabel('Accuracy') + plt.legend() + plt.title('Accuracy') + plt.tight_layout() + plt.savefig(os.path.join(results_dir,'accuracy.png')) + plt.close() + + plt.figure(figsize=(6,4)) + plt.plot(history.history.get('loss', []), label='train_loss') + plt.plot(history.history.get('val_loss', []), label='val_loss') + plt.xlabel('Epoch') + plt.ylabel('Loss') + plt.legend() + plt.title('Loss') + plt.tight_layout() + plt.savefig(os.path.join(results_dir,'loss.png')) + plt.close() +except Exception as e: + print(f"Visualization error (accuracy/loss): {e}") + +# 2) Confusion matrices for pattern and direction on test set +try: + y_true_pat = test_df['pattern'].map(pattern_to_idx).values + y_true_dir = test_df['direction'].map(direction_to_idx).values + + def plot_cm(y_true, y_pred, labels, fname, cmap=plt.cm.Blues): + cm = confusion_matrix(y_true, y_pred, labels=list(range(len(labels)))) + plt.figure(figsize=(5,4)) + plt.imshow(cm, interpolation='nearest', cmap=cmap) + plt.title(fname) + plt.colorbar() + tick_marks = np.arange(len(labels)) + plt.xticks(tick_marks, labels, rotation=45) + plt.yticks(tick_marks, labels) + thresh = cm.max() / 2. + for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): + plt.text(j, i, format(cm[i, j], 'd'), + horizontalalignment='center', + color='white' if cm[i, j] > thresh else 'black') + plt.ylabel('True') + plt.xlabel('Predicted') + plt.tight_layout() + plt.savefig(os.path.join(results_dir,fname.replace(' ','_') + '.png')) + plt.close() + + plot_cm(y_true_pat, pat_cls, pattern_classes, 'Pattern Confusion Matrix') + plot_cm(y_true_dir, dir_cls, direction_classes, 'Direction Confusion Matrix') +except Exception as e: + print(f"Visualization error (confusion matrices): {e}") + +# 3) PnL: histogram +try: + if len(pnl) > 0: + cum = np.cumsum(pnl) + plt.figure(figsize=(6,4)) + plt.plot(cum) + plt.xlabel('Trade #') + plt.ylabel('Cumulative PnL') + plt.title('Cumulative PnL') + plt.tight_layout() + plt.savefig(os.path.join(results_dir,'cumulative_pnl.png')) + plt.close() + + plt.figure(figsize=(6,4)) + plt.hist(pnl, bins=30) + plt.xlabel('Return') + plt.ylabel('Frequency') + plt.title('Trade Returns Distribution') + plt.tight_layout() + plt.savefig(os.path.join(results_dir,'pnl_hist.png')) + plt.close() +except Exception as e: + print(f"Visualization error (pnl plots): {e}") + +# 4) Sample predictions +try: + n = min(6, len(test_df)) + sample = test_df.sample(n=n, random_state=42) + plt.figure(figsize=(12,6)) + for i, idx in enumerate(sample.index): + img = plt.imread(test_df.loc[idx,'path']) + plt.subplot(2,3,i+1) + plt.imshow(img) + plt.axis('off') + tpat = pattern_classes[y_true_pat[idx]] + tdir = direction_classes[y_true_dir[idx]] + ppat = pattern_classes[pat_cls[idx]] + pdir = direction_classes[dir_cls[idx]] + plt.title(f'T:{tpat}/{tdir}\nP:{ppat}/{pdir}', fontsize=9) + plt.tight_layout() + plt.savefig(os.path.join(results_dir,'sample_predictions.png')) + plt.close() diff --git a/MidEval Code/MidEvalPS_rudrakumar/MIDEVAL_CODE_rudrakumar.py b/MidEval Code/MidEvalPS_rudrakumar/MIDEVAL_CODE_rudrakumar.py new file mode 100644 index 00000000..5636d233 --- /dev/null +++ b/MidEval Code/MidEvalPS_rudrakumar/MIDEVAL_CODE_rudrakumar.py @@ -0,0 +1,95 @@ +import pandas as pd +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler, OneHotEncoder +from sklearn.compose import ColumnTransformer +from sklearn.pipeline import Pipeline +from sklearn.linear_model import LogisticRegression +from sklearn.neural_network import MLPClassifier +from sklearn.metrics import ( + accuracy_score, + precision_score, + recall_score, + f1_score, + confusion_matrix +) +#data load and classified +df=pd.read_csv("data.csv") +X=df.drop("future_trend",axis=1) +Y=df["future_trend"] +cat_features=["asset_type","market_regime"] +num_features=[ + "lookback_days", + "high_volatility", + "trend_continuation", + "technical_score", + "edge_density", + "slope_strength", + "candlestick_variance", + "pattern_symmetry" +] +# preprocessing data part +preprocessing=ColumnTransformer( + transformers=[ + ("num", StandardScaler(), num_features), + ("cat", OneHotEncoder(drop="first"), cat_features) + ] +) +#data split part did lots of tweaking here to get best score +X_train,X_test,Y_train,Y_test=train_test_split( + X, + Y, + test_size=0.28, + random_state=7, + stratify=Y +) +#logisitc model train and its performance +log_model=Pipeline(steps=[ + ("preprocessor", preprocessing), + ("classifier", LogisticRegression(random_state=7, max_iter=500)) +]) +log_model.fit(X_train,Y_train) +Y_pred_log=log_model.predict(X_test) +print("logistic_reg model Performance:") +print("accuracy:", accuracy_score(Y_test, Y_pred_log)) +print("precision:", precision_score(Y_test, Y_pred_log)) +print("recall:", recall_score(Y_test, Y_pred_log)) +print("f1 score:", f1_score(Y_test, Y_pred_log)) +print("confusion matrix:\n", confusion_matrix(Y_test, Y_pred_log)) +#nn model train and its performance +nn_model=Pipeline(steps=[ + ("preprocessor", preprocessing), + ("classifier", MLPClassifier( + hidden_layer_sizes=(64, 32), + activation="relu", + solver="adam", + max_iter=500, + random_state=7 + )) +]) +nn_model.fit(X_train, Y_train) +Y_pred_nn=nn_model.predict(X_test) +print("") +print("neural_network model Performance:") +print("accuracy:", accuracy_score(Y_test, Y_pred_nn)) +print("precision:", precision_score(Y_test, Y_pred_nn)) +print("recall:", recall_score(Y_test, Y_pred_nn)) +print("f1 score:", f1_score(Y_test, Y_pred_nn)) +print("confusion matrix:\n", confusion_matrix(Y_test, Y_pred_nn)) +print("") +#created a small data showing result of both model +finaldata =pd.DataFrame({ + "model": ["logmodel", "nn_model"], + "accuracy": [accuracy_score(Y_test, Y_pred_log), accuracy_score(Y_test, Y_pred_nn)], + "precision": [precision_score(Y_test, Y_pred_log), precision_score(Y_test, Y_pred_nn)], + "recall": [recall_score(Y_test, Y_pred_log), recall_score(Y_test, Y_pred_nn)], + "f1_score": [f1_score(Y_test, Y_pred_log), f1_score(Y_test, Y_pred_nn)] +}) +print(finaldata) +#compared using if else logic for best model +if (accuracy_score(Y_test, Y_pred_nn) >= accuracy_score(Y_test, Y_pred_log)) and (f1_score(Y_test, Y_pred_nn) >= f1_score(Y_test, Y_pred_log)) and (precision_score(Y_test, Y_pred_nn) >= precision_score(Y_test, Y_pred_log)) and (recall_score(Y_test, Y_pred_nn) >= recall_score(Y_test, Y_pred_log)): + print("") + print("neuralnetwork model wins") +else: + print("") + print("logisitc model wins") \ No newline at end of file