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sdf #1

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280 changes: 280 additions & 0 deletions numpy_#1.ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "markdown",
"id": "1426cd93",
"metadata": {},
"source": [
"#### nympy task 1"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4c6dff0e",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "3124b9a3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1 6]\n",
" [ 2 8]\n",
" [ 3 11]\n",
" [ 3 10]\n",
" [ 1 7]]\n"
]
}
],
"source": [
"a = np.array([[1,6],\n",
" [2,8],\n",
" [3,11],\n",
" [3,10],\n",
" [1,7]])\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0c26a132",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2. 8.4]\n"
]
}
],
"source": [
"mean_a = np.mean(a, axis = 0)\n",
"print(mean_a)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "f548d9aa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(5, 2)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape"
]
},
{
"cell_type": "markdown",
"id": "08a460ef",
"metadata": {},
"source": [
"#### nympy task 2"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "bb310327",
"metadata": {},
"outputs": [],
"source": [
"a_centered = a - mean_a"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "2cc48f6a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-1. , -2.4],\n",
" [ 0. , -0.4],\n",
" [ 1. , 2.6],\n",
" [ 1. , 1.6],\n",
" [-1. , -1.4]])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_centered"
]
},
{
"cell_type": "markdown",
"id": "06c9cf90",
"metadata": {},
"source": [
"#### nympy task 3"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "d1d847ac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-2.4, -0.4, 2.6, 1.6, -1.4])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_centered.T[1]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "6c638ade",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-1.],\n",
" [ 0.],\n",
" [ 1.],\n",
" [ 1.],\n",
" [-1.]])"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_centered[:,:1]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "f2962a5f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.0"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_centered_sp = np.dot(a_centered.T[1], a_centered.T[0]) \n",
"a_centered_sp / (a_centered.shape[0] - 1)"
]
},
{
"cell_type": "markdown",
"id": "5437a025",
"metadata": {},
"source": [
"#### nympy task 4"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "23547665",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.0"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.cov(a.T)[0, 1]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "72bf00b5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1. , 2. ],\n",
" [2. , 4.3]])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.cov(a.T)"
]
}
],
"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": "3.10.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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