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finding mean
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‎projects/ML-Notebooks_Beginners/notebooks/maths/mean.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Mean [WIP]\n",
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"\n",
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"Mean also referred to as the arithmetic mean is a measure of the central tendency of a set of numbers. You can calculate the mean of a set of numbers as follows:\n",
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"\n",
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"\n",
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"$$\n",
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"m=\\frac{\\text { sum of the terms }}{\\text { number of terms }}\n",
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"$$\n",
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"\n",
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"or\n",
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"\n",
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"more formally:\n",
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"\n",
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"$$\n",
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"\\bar{x}=\\frac{1}{n}\\left(\\sum_{i=1}^{n} x_{i}\\right)=\\frac{x_{1}+x_{2}+\\cdots+x_{n}}{n}\n",
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"$$\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting torch\n",
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"tensorflow 2.13.1 requires keras<2.14,>=2.13.1, but you have keras 2.15.0 which is incompatible.\n",
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}
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],
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"source": [
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"#Uncomment to install pytorch library on python environment:\n",
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"# pip install torch\n"
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},
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{
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"text": [
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"tensor([[-1.2116, 1.0368, -0.4845],\n",
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" [-0.4369, -1.0795, 1.1041],\n",
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" [-1.2888, 0.3706, 0.2939],\n",
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" [-1.2139, -1.5166, 0.7118],\n",
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" [ 0.1386, 0.0818, -1.5839]])\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"tensor([[-0.2198],\n",
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" [-0.1374],\n",
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import torch\n",
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"x = torch.randn(5, 3)\n",
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"print(x)\n",
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"\n",
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"# use keepdim=True to preserve dimension\n",
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"x.mean(-1, keepdim=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor(42.)\n",
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"tensor(42.)\n"
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]
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}
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],
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"source": [
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"# Example 2:\n",
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"import torch\n",
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"\n",
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"x = torch.Tensor([4, 36, 45, 50, 75])\n",
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"\n",
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"print(torch.sum(x) / 5) # using sum and manual division\n",
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"print(torch.mean(x)) # using mean function"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### References\n",
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"\n",
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"- [Wikipedia](https://en.wikipedia.org/wiki/Mean)\n",
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"- [Basics of Statistics for ML Engineer](https://medium.com/technology-nineleaps/basics-of-statistics-for-machine-learning-engineers-bf2887ac716c)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.12 ('base')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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