From 72efa7c55747d971e2f1af440f7b8af988efab5f Mon Sep 17 00:00:00 2001 From: Marisan13 <96127669+Marisan13@users.noreply.github.com> Date: Mon, 20 Nov 2023 09:13:16 +0000 Subject: [PATCH] Lab done --- your-code/continuous.ipynb | 453 +++++++++++++++++++++++++++++++++---- your-code/discrete.ipynb | 167 +++++++++++--- 2 files changed, 547 insertions(+), 73 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..c45aff8 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -35,9 +35,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.18490485 2.44529571 2.84313362 2.78625763 2.65332816 2.93279581\n", + " 2.97139938 2.7514954 2.83879869 2.01563891]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", @@ -73,11 +82,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pylab import rcParams\n", + "from scipy.stats import bernoulli, binom, geom, poisson, uniform, expon, norm" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "def gen_uniform(bottom, ceiling, count):\n", + " x = uniform.rvs(size=count)\n", + " a = bottom\n", + " b = ceiling\n", + " return a + (b-a)*x" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Did not fixed the y-axis as it is easier to understand the feature as it is.\n", + "\n", + "result_1 = gen_uniform(10, 15, 100)\n", + "plt.hist(result_1) \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result_2 = gen_uniform(10, 60, 1000)\n", + "plt.hist(result_2) \n", + "plt.show()" ] }, { @@ -88,12 +161,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# your answer here:\n" + "- Comments: The variation of distribution is relatively smaller when the size (number of counts) is larger (in the latter case)." ] }, { @@ -114,11 +185,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "from scipy.stats import norm\n", + "\n", + "def gen_normal(average, std, count):\n", + " return norm.rvs(loc=average, scale=std, size=count)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result_1 = gen_normal(10, 1, 1000)\n", + "plt.hist(result_1, bins=100) \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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MakeModelYearEngine DisplacementCylindersTransmissionDrivetrainVehicle ClassFuel TypeFuel Barrels/YearCity MPGHighway MPGCombined MPGCO2 Emission Grams/MileFuel Cost/Year
0AM GeneralDJ Po Vehicle 2WD19842.54.0Automatic 3-spd2-Wheel DriveSpecial Purpose Vehicle 2WDRegular19.388824181717522.7647061950
1AM GeneralFJ8c Post Office19844.26.0Automatic 3-spd2-Wheel DriveSpecial Purpose Vehicle 2WDRegular25.354615131313683.6153852550
2AM GeneralPost Office DJ5 2WD19852.54.0Automatic 3-spdRear-Wheel DriveSpecial Purpose Vehicle 2WDRegular20.600625161716555.4375002100
3AM GeneralPost Office DJ8 2WD19854.26.0Automatic 3-spdRear-Wheel DriveSpecial Purpose Vehicle 2WDRegular25.354615131313683.6153852550
4ASC IncorporatedGNX19873.86.0Automatic 4-spdRear-Wheel DriveMidsize CarsPremium20.600625142116555.4375002550
\n", + "
" + ], + "text/plain": [ + " Make Model Year Engine Displacement \\\n", + "0 AM General DJ Po Vehicle 2WD 1984 2.5 \n", + "1 AM General FJ8c Post Office 1984 4.2 \n", + "2 AM General Post Office DJ5 2WD 1985 2.5 \n", + "3 AM General Post Office DJ8 2WD 1985 4.2 \n", + "4 ASC Incorporated GNX 1987 3.8 \n", + "\n", + " Cylinders Transmission Drivetrain Vehicle Class \\\n", + "0 4.0 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD \n", + "1 6.0 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD \n", + "2 4.0 Automatic 3-spd Rear-Wheel Drive Special Purpose Vehicle 2WD \n", + "3 6.0 Automatic 3-spd Rear-Wheel Drive Special Purpose Vehicle 2WD \n", + "4 6.0 Automatic 4-spd Rear-Wheel Drive Midsize Cars \n", + "\n", + " Fuel Type Fuel Barrels/Year City MPG Highway MPG Combined MPG \\\n", + "0 Regular 19.388824 18 17 17 \n", + "1 Regular 25.354615 13 13 13 \n", + "2 Regular 20.600625 16 17 16 \n", + "3 Regular 25.354615 13 13 13 \n", + "4 Premium 20.600625 14 21 16 \n", + "\n", + " CO2 Emission Grams/Mile Fuel Cost/Year \n", + "0 522.764706 1950 \n", + "1 683.615385 2550 \n", + "2 555.437500 2100 \n", + "3 683.615385 2550 \n", + "4 555.437500 2550 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "data = pd.read_csv(\"vehicles.csv\")\n", + "display(data.head())\n", + "\n", + "plt.hist(data[\"Fuel Barrels/Year\"])\n", + "plt.show()" ] }, { @@ -174,11 +472,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(data[\"CO2 Emission Grams/Mile\"])\n", + "plt.show()" ] }, { @@ -190,11 +500,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(data[\"Combined MPG\"])\n", + "plt.show()" ] }, { @@ -205,12 +527,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# you answer here:\n" + "- Comments : \"Combined MPG\". As it is more symmetric about the mean compared to the others." ] }, { @@ -237,11 +557,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def gen_expon(mean):\n", + " return np.random.exponential(scale = mean, size = 1000)\n", + "\n", + "mean_one = gen_expon(1)\n", + "mean_hundred = gen_expon(100)\n", + "\n", + "plt.hist(mean_one, bins = 100)\n", + "plt.show()\n", + "plt.hist(mean_hundred, bins = 100)\n", + "plt.show()" ] }, { @@ -252,12 +602,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# your answer here:\n" + "- Comments : The sequence with a mean of 1 shows a peak around lower values and have a higher frequency of values near zero and decrease more rapidly as values increase compared to the one of mean of 100. Also the spread (range of values) is relatively shorter compared to the latter." ] }, { @@ -273,12 +621,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7534030360583935\n" + ] + } + ], "source": [ - "# your answer here\n", - "# Hint: This is same as saying P(x<15)\n" + "# Hint: This is same as saying P(x<15)\n", + "\n", + "lambda_inv = 10/1\n", + "expon_dist = expon(scale = lambda_inv)\n", + "print(expon_dist.cdf(14))" ] }, { @@ -290,17 +649,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2231301601484298\n" + ] + } + ], "source": [ - "# your answer here\n" + "print(1-expon_dist.cdf(15))" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -314,7 +681,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb index 310a3c6..2d25023 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -17,6 +17,19 @@ "- Round the final answer to three decimal places." ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import bernoulli, binom, geom, poisson, uniform, expon, norm\n", + "import random" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -33,17 +46,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Apple: 0.6\n", + "Orange: 0.4\n" + ] + } + ], "source": [ - "\"\"\"\n", - "Calculate:\n", - "p = probability that the fruit is an apple \n", - "q = probability that the fruit is an orange\n", - "\"\"\"\n", + "p = 60/100\n", + "q = 40/100\n", "\n", - "# your code here\n" + "print(f\"Apple: {bernoulli(p).pmf(1)}\")\n", + "print(f\"Orange: {bernoulli(q).pmf(1)}\")" ] }, { @@ -61,11 +81,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.078\n", + "0.0\n" + ] + } + ], "source": [ - "# your code here\n" + "apple = (bernoulli(p).pmf(1))**5\n", + "orange = (bernoulli(q).pmf(1))**15\n", + "\n", + "print(round(apple,3))\n", + "print(round(apple*orange,3))" ] }, { @@ -83,11 +116,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.001\n" + ] + } + ], "source": [ - "# your code here\n" + "n = 20\n", + "binomial_dist = binom(n,p)\n", + "apple = binomial_dist.pmf(5)\n", + "\n", + "print(round(apple,3))" ] }, { @@ -101,11 +146,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "round(binomial_dist.cdf(4),3)" ] }, { @@ -119,12 +175,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n", - "# Please label the axes and give a title to the plot\n" + "X = np.arange(0,20)\n", + "plt.plot(X, binomial_dist.cdf(X), \"o\")\n", + "plt.show()" ] }, { @@ -151,11 +219,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.054" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "mu = 2.3\n", + "poisson_dist = poisson(mu)\n", + "round(poisson_dist.pmf(5),3)" ] }, { @@ -167,18 +248,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n", - "# Please label the axes and give a title to the plot \n" + "X = np.arange(0, 10)\n", + "plt.plot(X, poisson_dist.pmf(X), \"o\")\n", + "plt.show()\n", + "\n", + "X = np.arange(0, 10)\n", + "plt.plot(X, poisson_dist.cdf(X), \"o\")\n", + "plt.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -192,7 +299,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.3" } }, "nbformat": 4,